# Internet of Mirrors for Connected Healthcare and Beauty: A Prospective
Vision

Haneen Fatima  James Watt School of Engineering, University of Glasgow,
Glasgow, G12 8QQ, UK Muhammad Ali Imran  James Watt School of Engineering,
University of Glasgow, Glasgow, G12 8QQ, UK Ahmad Taha  James Watt School of
Engineering, University of Glasgow, Glasgow, G12 8QQ, UK Lina Mohjazi  James
Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK

###### Abstract

With the shift towards smart objects and automated services in many
industries, the health and beauty industries are also becoming increasingly
involved in AI-driven smart systems. There is a rising market demand for
personalised services and a need for unified platforms in many sectors,
specifically the cosmetics and healthcare industries. Alongside this rising
demand, there are two major gaps when considering the integration of
autonomous systems within these sectors. Firstly, the existing smart systems
in the cosmetics industry are limited to single-purpose products and the
employed technologies are not widespread enough to support the growing
consumer demand for personalisation. Secondly, despite the rise of smart
devices in healthcare, the current state-of-the-art services do not fulfil the
accessibility demands and holistic nature of healthcare. To bridge these gaps,
we propose integrating autonomous systems with health and beauty services
through a unified visual platform coined as the Internet-of-Mirrors (IoM), an
interconnected system of smart mirrors with sensing and communication
capabilities where the smart mirror functions as an immersive visual dashboard
to provide personalised services for health and beauty consultations and
routines. We aim to present an overview of current state-of-the-art
technologies that will enable the development of the IoM as well as provide a
practical vision of this system with innovative scenarios to give a forward-
looking vision for assistive technologies. We also discuss the missing
capabilities and challenges the development of the IoM would face and outline
future research directions that will support the realisation of our proposed
framework.

###### Index Terms:

Internet of Things, Assistive Technologies, Smart Mirrors, Beauty Tech.

##  I Introduction

Figure 1: IoT example ecosystems and possible interactions.

The emergence of the IoT has significantly supported the shift towards
increased interconnectivity and automation in many sectors such as healthcare,
transportation, and smart cities [1], [2]. The increasing number of ‘things’,
internet-connected embedded systems, has opened possibilities for numerous
everyday physical objects to interact with each other and achieve shared goals
such as enhanced energy efficiency, improved health monitoring, and
streamlined supply chains [3, 4, 5] In recent years, in homes, offices and
even industrial contexts, there has been an upsurge of smart environments,
appliances and devices that all connect wirelessly over the internet or
through local networks. The increased deployment of IoT has led to the
development of various interconnected frameworks that fall under the umbrella
of IoT such as the Internet of Vehicles (IoV) [6] and the Internet of Musical
Things (IoMusT) [7]. There has been an increased research focus in multiple
domains such as transportation and smart cities but there is minimal academic
research on the application of IoT technologies in the cosmetics and beauty
industries. Comparatively, there are ongoing significant research efforts for
IoT in healthcare with the rise of telemedicine. However, there is still a
need for enhanced and seamless connectivity to expand the reach and
accessibility of healthcare systems to the public and enable advanced use-
cases. This will not only reduce healthcare staff workload but also improve
the patient experience on a holistic level to eventually support better
healthcare outcomes [8]. Additionally, this research focus often relates to
applications such as emergency management, post-discharge care through health
monitoring and patient authentication [9] but there is a notable lack of
academic attention given to specialised and elective healthcare services like
dermatology, dentistry, and optometry. Many aspects of elective healthcare
services overlap with the cosmetics industry indicating the potential for a
unified platform that could encompass both sectors.

There is an increasing prevalence and demand for AI-driven services and
personalisation in the cosmetics and beauty industry with the integration of
technologies like IoT and Augmented Reality (AR) to provide services such as
personalised product recommendations and cosmetic colour customisers [10].
This demand for personalisation is highlighted in an Accenture survey [11]
which found that 75% of consumers are more likely to make a purchase when
recommendations are tailored to them. Similarly, a Forrester study [12] found
that 77% of consumers have chosen, recommended, or paid extra for a brand that
offers a personalised service or experience. The current capabilities of
commercially available smart beauty products, often termed beauty tech, are
limited to information delivery and product customisations with services like
portable skin analysers, cosmetic curation apps and skin analysis apps [13].
Although these products provide personalised user experiences, they are not
widespread and accessible and serve only a single purpose of service. This
indicates the need for a multi-purpose unified platform that can offer
personalised services. This issue extends into elective healthcare or
aesthetic medicine as well in areas such as dentistry and optometry. In these
areas although there are smart services such as diagnostic support [14] and
mobile app-integrated smart medical instruments [15], these also only serve a
single purpose and do not provide the clinician or patient with a holistic
view of the treatment process.

The nature of personalised services in the cosmetics and beauty industry and
the elective healthcare sector are visual. An existing smart system that has
the potential to support these services is a smart mirror. A smart mirror
refers to an intelligent interface that operates as a mirror and can interact
with users and display various responses and information. Currently, smart
mirror products are becoming increasingly available for assistive beauty and
assistive living applications. The common features of most smart mirrors
include the display of multimedia data and the option for the user to interact
with contextual information such as weather conditions and interaction with
options such as voice recognition or a touch screen dashboard [16]. Within
beauty tech, there is an increasing market for smart mirrors that offer
results-driven services such as virtual makeup try-on or product
recommendations [17]. However, drawbacks of beauty tech smart mirrors have
been found in consumer interviews conducted by Yum et al [18] which revealed
three main limitations, namely; lack of personalisation to individual/consumer
needs; lack of research on data exchange between smart devices and the smart
mirror (i.e. the IoT environment); and limited AR features such as real-time
virtual try-on. In general, regardless of the smart mirror application,
whether it is for the smart home environment or beauty tech, smart mirror
products are currently limited to stand-alone systems.

To the best of our knowledge, there is no existing vision or concept of
interconnected IoT-based smart mirrors. Capitalising on this, this article
aims to provide a unified vision for an interconnected system of mirrors
coined as the Internet of Mirrors (IoM) under the IoT framework. This system
will be conceptualised with the existing concept of smart mirrors to support
assistive technologies, primarily the health and beauty sector. We will focus
on defining the IoM ecosystem as well as discussing the state-of-the-art
technologies that will enable the existence of the IoM. This article seeks to
bridge the gaps between an implementation-focused approach to smart mirrors
and the need for interconnected services in the assistive health and beauty
sectors. The gap between the industrial development of smart mirrors and
beauty tech and the corresponding lack of development in this domain in
academia will also be highlighted.

New opportunities may be fostered by the IoM for not only assistive beauty but
smart living in general, leading to applications and services that can utilise
the visualisation capabilities of the IoM to connect the physical and digital
realms [19]. However, the realisation and deployment of the IoM for end users
would also face a number of challenges which will be identified and discussed
in this paper. These include low-latency and high-reliability communication
protocols for real-time visualisations and services, interoperability and
standardisation for the integration of multi-modal content and user privacy
and security concerns.

Figure 2: Overall IoM ecosystem.

The rest of this paper is structured as follows, Section II defines the
concept and ecosystem of the envisioned IoM. Section III highlights and
surveys existing technologies and works that will support the development and
implementation of the IoM. Section IV outlines several proposed scenarios
related to the IoM in order to identify potential capabilities of the system
as well as current missing capabilities. Section V describes the main
challenges that the development of such a system could face as well as
potential solutions and finally, our main conclusions and future research
direction are summarised in Section VI.

##  II Concept and Vision

The IoM is a novel concept that can be considered an ecosystem or a subfield
of IoT. The vision for this ecosystem is for it to interact with other
ecosystems and smart systems like the IoV, IoMusT or Internet of Medical
Things (IoMT) [20] as well as being able to interact with the Things within
these systems. In Fig.1 the possible interactions between IoT ecosystems have
been visualised where IoX represents an X number of other possible ecosystems
within the IoT framework.

As a system, the IoM can be defined as, “the integration of smart mirror
interfaces and related or embedded sensors over a connected network that can
support personalised services and user metric visualisation based on sensed
data from interactions between the user and smart mirror or interactions
between the smart mirrors themselves.” In this context we consider a smart
mirror to be a mirror that can sense, visualise and interact with the user and
other components of the IoM ecosystem. The ecosystem of these smart mirrors
involves collective elements like computing, storage, data exchange and
resource management, all of which are garnered by unified protocols and
standards.

Considering IoT ecosystems in general [21, 22, 23], the IoM can have diverse
ecosystems depending on the application and its scope. The IoM ecosystem, at
its fundamental level, involves the users (e.g. consumers, patients, cosmetic
and health professionals) and smart mirror interfaces, however from an
interconnectivity perspective this system can be categorised into three
elements, which are detailed in the subsections below.

The elements of the IoM can also be described in terms of layers similar to a
general IoT layer structure [1], where the smart mirrors act as the sensing
and transmission layer, and the connectivity of the system can be considered
as the network and communication layer. Finally, the services component
corresponds to an application layer. The overall IoM ecosystem with its
components and layers is depicted in Fig.2.

###  II-A Smart Mirrors

Figure 3: An end-to-end platform of a single smart mirror. Table I: Examples of 5G and LPWAN integration in smart mirrors. Wireless Technology |  Feature |  Explanation
---|---|---
5G |  Cloud Connectivity |  Connection with fifth-generation (5G) optimised cloud services would allow smart mirrors to access and store data efficiently and quickly.
|  App Integration |  Built-in 5G compatible apps can utilise 5G’s high speeds and low latency communication for the delivery of high quality services and analytics.
|  WiFi Hotspot |  The smart mirrors can act as wireless access points which would allow other devices to connect to 5G networks through them.
LPWANs |  Asset Tracking |  Tracking of the location of physical assets and other personal items would allow users to locate their items quickly and easily.
|  Energy Management |  Integration with LPWAN-enabled energy management systems would enable smart mirrors to monitor and adjust energy consumption in real time.
|  Smart Home Integration |  Integration with smart home systems that use LPWANs would enable smart mirrors to communicate with other devices in the home, such as smart locks, smart thermostats, and security systems.
|  Remote Monitoring |  Users could monitor their mirror’s usage and performance remotely to allow for ease in maintenance and troubleshooting.

The envisioned concept of the IoM is for a number of smart mirror devices,
along with their sensed data, to be interconnected and have access to other
IoT devices. The smart mirror devices in the IoM system can be considered to
inherit the characteristics of Things as its subclass. These characteristics
include attributes like sensors, actuators, data collection, analysis and
transmission options as well as networking and connectivity features.

More specifically, a smart mirror will be considered a complete component
including all its sensors and processing capabilities, albeit different smart
mirrors in the ecosystem can vary from being just a connected interface to a
full system with embedded sensing and processing capabilities. However, any
smart mirror in the system will have the capability to act as a receiver
and/or transmitter by being connected to the relevant local or remote network.
A schematic of an end-to-end platform of a single smart mirror is shown in
Fig. 3.

With the growing commercial smart mirror market [24], a system of
interconnected mirrors could enable many new opportunities not only for the
cosmetics and health industries but also for other domains such as smart homes
and smart education. The visualisation aspect of the smart mirror interface
also has the potential to transform user accessibility for consumers and
patients by offering a more enhanced and innovative health and beauty
experience.

###  II-B Connectivity

The connectivity of the IoM ecosystem is fundamental to its operation and
development. To support the envisioned multi-directional communication between
smart mirrors and from smart mirror to the user (and vice versa), appropriate
network protocols and standards must be considered. Depending on the
application and domain, different local and remote wireless communication
protocols can be deployed. The connectivity of the IoM would facilitate the
processing of computationally heavy commands and requests to off-load the
computation from the embedded micro-controller in the smart mirror. Although
the applications of the IoM in the cosmetics industry are not time critical in
comparison to health applications, real-time use cases such as remote
consultancy would still require the network to fulfil low latency, high
reliability and synchronisation requirements. The connectivity element of the
IoM will not only enable interconnection between IoM smart mirrors and devices
but could also facilitate additional services to support the user experience.
Table I provides an overview of two key wireless technologies, 5G and Low
Power Wide Area Networks (LPWANs), and possible features that could be enabled
by them in the IoM.

Figure 4: Interaction possibilities between consumers, professionals and
service providers.

###  II-C Applications and Services

Whether the application domain for the IoM is assistive health, beauty or
living, there are several services that can be developed from the sensed and
communicated data from the smart mirrors and their connectivity features.
These services can target consumers (beauty and cosmetic customers, patients),
professionals (cosmeticians, dermatologists, doctors) and service providers
(brands, stores, clinics). To establish an efficient interactive interface of
the smart mirrors, these services will need to consider real-time computation
and requirements. Fig.4 shows the possible remote and co-located interactions
between different users in the IoM ecosystem.

##  III Enabling Technologies

In this section, a list of state-of-the-art technologies related to the IoM is
presented. We aim to describe the results of various application domains that
lead to the conceptualisation of the IoM. A top-level overview of the
presented technologies and their potential integration in the IoM is shown at
the end of this section in Table III.

###  III-A Standalone Smart Mirrors

The current usage of the term ”smart mirror” refers to a mirror that is
enhanced with various features, such as touchscreens, voice assistants and
other digital displays [16]. Smart mirrors are designed to offer users a more
interactive and personalised experience while using the mirror. In the late
2000s and early 2010s, research related to smart mirrors referred to live
webcam footage and implemented services on the live feed as a smart mirror
[25], [26].

One of the earliest examples of a modern-day smart mirror is the ”Mirror,
Mirror on the Wall” project by Michael Teeuw, which was first introduced in
2014 [27]. Teeuw used a Raspberry Pi computer, a monitor, a two-way mirror,
and various other components to create a customised mirror with a digital
display that could show news, weather, and other information. Similar to this
project, the majority of literature on smart mirror development focuses on the
mirror’s technical implementation and hardware of smart mirrors with embedded
microcontrollers namely Raspberry pi [28] and occasionally Arduino Uno [29].
The most common features implemented even in more recent studies are live
weather, time, calendar and music display integrated either through a web API
or through a connection to a smartphone [30, 31, 32, 33].

In recent years, there have been some increased research efforts within this
domain to develop and integrate new features such as the use of Infrared (IR)
sensors for user presence detection [34], gesture control and tracking for
music control [35] as well as WiFi connectivity [36] for mobile
synchronisation with the digitally displayed data [37]. There have also been
efforts in the development of plugins and web APIs to support smart mirror
applications [38] [39]. There has also been an increase in the number of
commercially available smart mirrors for both assistive beauty and assistive
living applications. There are many commercial options of stand-alone mirrors
for assistive beauty purposes as well as assistive health. Commonly available
smart mirrors with their main features have been shown in Table II.

Table II: Commercially available smart mirrors and their features. Name |  Features
---|---
Sephora Mirror [40] |  QR code generation for in-store and online makeup, skincare and fragrance recommendations
HiMirror [41] |  Adjustable lighting, skin analysis, product recommendation, cosmetics tutorial
MemoMi [42] |  Image capturing and storage,AR eyewear and clothes, Smartphone and SNS sharing
Charlotte Tilbury MagicMirror [43] |  Augmented makeup, importing images
Shiseido Digital Counseling Mirror [44] |  Product information, application instructions, skin check, smartphone compatibility
Panasonic New Magic Mirror [45] |  Skin condition identification, virtual try-on
Modiface [46] |  Product recommendation, virtual try-on
Lumini Kiosk V2 [47] |  Skin analysis, product recommendation, skin comparison with other users
Icon AI Z Mirror [48] |  Adjustable lighting, skin analysis, product recommendation, virtual try-on
Morror [49] |  Adjustable lighting, cosmetics tutorial, weather information
Simplehuman Sensor Mirror Pro [50] |  Smart lighting
Kohler Verdera Voice Lighted Mirror [51] |  Voice activation integrated with Alexa and Google assistant
iHome Reflect Smart Vanity Mirror [52] |  Lighting and speaker control with smartphone
Capstone Connected Home Smart [53] |  Integration with other smart home devices, built-in speaker, voice activation

The commonality between all these aforementioned commercially available
systems is the lack of features in a standalone smart mirror system. However,
the smart mirrors catered for cosmetics and beauty have relatively more varied
features than those targeted for assisted living use. Comparatively, in
academia, there are limited features and services that cater to assistive
beauty because the main domain and application of smart mirrors are for
assisted living [54] [55]. A relevant example is the development of a chatbot
integrated into the smart mirror for the elderly [56]. Similarly, in Yang et
al [57], an error-resistant movement detection algorithm is integrated into
the smart mirror for increased accuracy for pose estimation for the elderly.
Pose estimation from smart mirrors has also been considered in a fitness
context where Hippocrate et al have developed a smart mirror to automatically
detect and count repetitions of exercises [58]. This opens up possibilities
for the accurate detection of joint and/or mobility-related health conditions
for the elderly as well as guided fitness coaching [59]. Specifically for
assistive health, the main focus and use of smart mirrors are currently
services like health monitoring [60] with the processing of multi-modal
biomedical data for hospital or clinic settings [61].

Figure 5: A simple IoT-enabled smart mirror schematic.

Smart mirror applications and services also include user-specific recognition
systems for personalised user metrics and personalised recommendations. This
includes facial recognition where facial expressions have been analysed and
classified to detect premenstrual syndrome symptoms [62], deliver personalised
content [63], control lighting based on mood [64] and security for smart
living applications by verification of user identity through facial and voice
authentication [65] [66]. These recognition and detection methods have the
potential to increase personalisation in the cosmetics industry and give
accurate product recommendations such as in Nguyen et al where a makeup
recommendation and synthesis method is proposed [67]. Although the synthesis
of the product is over a still image, AR technologies can be combined with
this method to enable personalised and accurate virtual try-on in real-time.
An example for skincare applications exists in [68] where a smart mirror is
used for skin-type classification and identification, although the system only
gives the user the result of the identification there is potential for product
recommendations to be integrated in the beauty tech context as well as
healthcare context for skincare product recommendation.

Since the developed prototypes in most mentioned works have two or three
limited features, the processing is feasible with a server integrated on a
stand-alone smart mirror. However, future larger-scale smart mirrors or IoM
ecosystems comprising of inter-connected smart mirrors with several integrated
features, will entail the need to deploy distributed computing approaches. The
inclusion of increased features also requires energy and power consumption
considerations, this has been preliminarily considered by Rat et al [69] who
presented a comparison of the power consumption of different operating systems
and different smart mirror modules, such as voice recognition and live weather
updates, by enabling and disabling these features. Solutions for mitigating
the effects of power-intensive tasks are presented as a trade-off between
processing on the cloud and processing latency.

Additional research gaps that are apparent from the state of the art of
current smart mirrors include low-quality resolution of the smart mirror due
to the nature of its hardware construction. Current studies construct the
smart mirror by the addition of a reflective surface such as an acrylic sheet
to an LED display so as to act as a two-way mirror. However, the optical
resolution of the reflective surface is comparatively lower than a regular
glass mirror, hence there is potential to optimise and improve the fundamental
composition of the smart mirror hardware components such as incorporating
anti-reflective coating, using a glass 2-way mirror [70] or the use of a
transparent OLED display. Finally, there is a qualitative research gap in the
smart mirror research area due to the lack of empirical evidence on the
effectiveness of smart mirrors in improving the user experience through its
smart living applications [18].

Capitalising on the aforementioned studies, we illustrate a typical modern-day
stand-alone smart mirror in Fig.5.

###  III-B Skin Imaging Methodologies

Skin imaging technologies have advanced significantly in recent years. Some
common advanced image processing techniques that are being used in the
analysis of skin imagery include classification, feature extraction and
segmentation. Skin imaging technologies can serve as a powerful tool for
disease detection in assistive health scenarios [71]. By identifying and
classifying skin features, these technologies can assist in the early
detection and diagnosis of various skin conditions, including melanoma,
eczema, and acne. Early detection is crucial in managing these conditions and
can significantly improve treatment outcomes. For instance, melanoma detected
and treated early is almost always curable, while if left untreated, it can
quickly spread to other parts of the body and become fatal [71].

  * •

Classification and feature extraction are integral components of image
processing that work in collaboration to analyse skin images. First, specific
skin characteristics, such as texture, colour, and shape, are identified and
quantified using feature extraction techniques. These features are then
categorised based on predefined criteria through classification. Both
processes are commonly facilitated by Machine Learning (ML) algorithms, with
convolutional neural networks (CNNs) being a popular choice due to their
effectiveness in image analysis [72]. An example of this is in [73] where the
authors use CNNs to classify non-clinical skin imagery, specifically facial
skin patches, by normal skin, spots and wrinkles. The study found potential
for increased accuracy if a larger dataset was collected. Similarly, in [74]
skin imagery of the forearm is used to extract features of skin texture and
use classification for personal identification. However, in this study
clinical imagery was collected from a dermoscope and a capacitive imaging
device. By comparing both studies, both non-clinical and clinical imagery like
capacitive imaging have the potential to be integrated into a larger multi-
modal dataset to create a model with increased accuracy.

  * •

Capacitive imaging is often used for skin hydration measurements, this can be
combined with classification techniques such as in [75] where the
classifications of different hydration levels were used to identify different
regions of the body. This non-invasive method to monitor skin hydration levels
can contribute further insights into the skin’s overall health analysis and
can potentially accurately guide skincare treatments recommended by the
envisioned IoM.

  * •

Segmentation can also be used as a pre-processing stage before feature
extraction to separate the skin image into different regions or objects to
isolate relevant areas. This can specifically target areas like lesions for
diagnosis support and treatment planning. This can be seen in [76] where the
authors segment the input image to isolate skin lesions and extract different
features like asymmetry, colour and size for melanoma detection. The
combination of segmentation, feature extraction and classification techniques
for skin image processing allows for more accurate detection of skin
conditions and opens opportunities for increased accuracy not only in
diagnosis but also in skincare treatment recommendations.

  * •

Disease detection has also benefited from the aforementioned image-processing
techniques, such as in [77] and [78], where the authors identify different
skin conditions such as melanoma using pre-trained CNNs. In a more recent
study [79], a similar methodology is built upon to incorporate the detection
of other skin conditions like eczema, psoriasis and acne with a larger
dataset. There are also studies related to the detection of acne such as in
[80] where the acne is categorised by a seven-classifier. Likewise in [81],
feature extraction and classification are used for acne detection by its
severity but the study also integrates this service into a mobile application
which can detect acne from a self-taken image. Moreover, multispectral imaging
techniques [82] are also particularly effective in disease detection [83]
[84]. These techniques enable an analysis of skin properties below the surface
that are not visible to the naked eye, providing a deeper understanding of
various skin conditions [85].

  * •

Wearable skin sensors and dermatoscopes have also made skin imaging more
accessible and practical [86]. These devices can capture high-resolution
images of the skin, allowing for detailed analysis and assessment [87]. This
is particularly useful in remote healthcare scenarios, where patients can use
these devices to capture images of their skin [88], which could then be
analysed by remotely connected dermatologists or AI-enabled detection systems
embedded into a prospective IoM for diagnosis and treatment planning.

Overall, the integration of skin imaging technologies with smart mirrors and
the foreseen IoM could provide users with a more comprehensive view of their
skin’s condition and enable them to make informed decisions about skincare and
makeup products. These technologies are expected to assist in the diagnosis
process by offering detailed and labelled images and analyses of the skin,
along with suggested diagnoses. For example, by utilising classification and
feature extraction techniques, dermatologists will be able to identify and
quantify specific skin characteristics from the image, such as texture,
colour, and shape. This is anticipated to aid in the diagnosis of various skin
conditions and guide treatment decisions. Furthermore, the use of segmentation
techniques will allow dermatologists to focus directly on isolated relevant
areas in skin images, such as skin lesions, automatically highlighted by the
future IoM. AI-driven skin analysis is projected to significantly reduce
diagnosis time and improve the accuracy of diagnoses. This efficiency will be
particularly beneficial in dermatology, where timely and accurate diagnoses
are critical. Future trends in skin imaging may include more accurate
diagnosis, early detection of skin conditions, and personalised treatment
planning. However, these advancements also bring forth challenges related to
image quality, data privacy, and regulatory compliance, which would need to be
addressed to ensure the successful implementation of these technologies.

###  III-C Real-Time Health Monitoring

Real-time health monitoring is a major area of focus within assistive health.
By combining the capabilities of non-invasive sensing, data analytics and
instant feedback, the IoM can be integrated with real-time health monitoring
to improve the patient experience in a convenient and non-intrusive way as
well as ease the consultancy process for health professionals in terms of
early detection, prevention, and better management of health conditions.

Within real-time health monitoring, vitals tracking can provide a wealth of
data for diagnostics and health assessments. We foresee that Non-contact
sensing technologies like Radio Frequency (RF) sensing, photoplethysmography
(PPG) and infrared thermography can be integrated in the IoM to continuously
monitor critical physiological parameters to support assistive health
applications.

  * •

PPG, for instance, uses light to measure changes in blood volume, enabling the
tracking of heart rate, respiratory rate, and oxygen saturation levels [89].
The technique is particularly advantageous for continuous, non-invasive
monitoring and can be implemented using simple, cost-effective hardware. These
metrics can also be tracked using a digital camera such as in [90] where the
authors use the colour information of patient video footage to detect the
blood pulse volume to estimate these metrics. In [91] this method of heart
rate variability tracking using an RGB camera is improved by separating the
source from the noise in the video recording of the subject. Similarly to
cameras, microphones, which are often integrated into many smart mirrors, can
also be used to capture data on breathing patterns which can be used to
estimate heart rate and other indicators of health.

  * •

Infrared thermography is also a non-contact imaging technique that provides a
different approach by capturing the infrared radiation emitted by an object
using an IR camera, typically in the form of heat. The infrared radiation is
then converted into an electronic signal, which is processed to produce a
thermal image on a video monitor and perform temperature calculations [92].
Since the skin’s temperature distribution can be influenced by a variety of
physiological and pathological factors, thermal images can provide valuable
diagnostic information such as patterns of inflammation, vascular activity and
metabolic processes [93]. This information can contribute to the early
detection and diagnosis of various skin conditions like psoriasis, dermatitis
and localised scleroderma [94]. Monitoring changes in skin temperature over
time with infrared thermography can also provide insights into the progression
of diseases and the effectiveness of treatments. A recent study uses this
sensing technology to monitor the respiration rate (RR) by using ML to
differentiate normal and abnormal breaths by predefined thresholds and obtain
the breaths per minute, without any manual intervention as compared to
traditional RR monitoring methods [95]. This study shows the potential for
non-contact sensing to monitor and predict not only skin conditions but also
overall health conditions since respiration is a vital physiological process
and can be an indicator of different breathing disorders.

  * •

RF sensing encompasses a broad category of technologies that use radio waves
to detect changes or events. Shah and Fioranelli provided a comprehensive
review of RF sensing technologies, highlighting their potential in assisted
daily living and healthcare settings as a less intrusive means of health
monitoring [96]. Radar sensing, as part of the broader category of RF sensing
technologies, also offers significant potential for non-contact vitals
tracking. We anticipate its integration into the smart mirror sensing system
in the IoM ecosystem with a device such as the Xethru X4 Impulse-Radio Ultra-
Wideband (IR-UWB) radar system on a chip [97] which can be used to monitor
body movements, including breathing and heart rate, without the need for
physical contact or sensors [98]. This can be achieved by measuring low-power
radio waves emitted towards the user and measuring the frequency shift of the
waves as they bounce back from the user’s body. Frequency changes in the waves
can be measured and used to detect body movements, including chest movements
associated with breathing and heart rate. RF sensing can also be used to
monitor more complex physiological parameters by detecting small displacements
of the skin surface associated with arterial pulsation, providing an indirect
measure of heart rate and blood pressure. This could be particularly useful
for the continuous monitoring of patients with cardiovascular disease [99].
Further advancements in RF sensing are seen in the development of high-
resolution chipless tags for RF sensors, as presented by Abbasi et al in
[100]. These tags enable more detailed and accurate data collection, enhancing
the potential for detailed health monitoring and diagnostics. Comparatively, a
more unique application of compact RF sensing is explored by Zheng et al in
their concept of ”V2iFi” vehicle vital sign monitoring [101]. This concept
exhibits the flexibility and adaptability of RF sensing methods [102],
suggesting their applicability in diverse settings, including smart mirrors
and the anticipated IoM.

In our envisioned IoM, aside from integrating real-time health monitoring
through cameras and sensors, the smart mirror will also have the capability to
utilise data from wearables to provide a holistic view of an individual user’s
health metrics [103]. Future wearable devices equipped with RF sensors are
expected to range in applications from monitoring respiratory patterns [79] to
detecting tremors in patients with Parkinson’s disease [104]. Parallel to
wearables, the emerging paradigm of ’skintronics’ or skin-interfaced
electronics, which will merge with the user’s skin, holds high potential for
achieving increased accuracy in health metrics monitoring [105]. Additionally,
personalised health monitoring is envisioned to be enhanced by smart textiles
[106], where circuits and systems will be integrated into everyday clothing
[107]. Haptic feedback technologies are also expected to embed tactile
experiences directly into these textiles, offering users an enhanced, multi-
modal interaction [108]. These technologies are not just for monitoring but
also for interactive alerts and warnings. We anticipate that these wearable
technologies will interact seamlessly with the future IoM to offer real-time
feedback, tailored health recommendations, and tools for proactive health
management.

###  III-D Computer Vision

Computer vision is a significant area of focus for makeup-related applications
[109] and is often used as a final stage after user feature detection and
implementation of a recommendation system. In our envisioned IoM, computer
vision will have the capability to augment cosmetics or provide AR experiences
on user images. These future systems, relying on live camera footage and
infrared cameras, will be able to superimpose different makeup products
virtually on the user in real-time, offering a highly interactive and engaging
user experience.

Several studies exist on the augmentation of cosmetics or AR on user images
with the earliest in 2009 [25] [26], where the authors propose a smart mirror
system composed of a live web camera footage integrated with an IR camera for
position tracking, to augment different makeup products on user virtually.
Similarly, in recent years more sophisticated and user-centric approaches are
emerging such as in [110] where makeup is augmented through a tactile
experience by allowing the user to use their finger to simulate a physical
applicator. Their initial prototype could accurately detect touch from 2.2mm
and also achieved real-time interactive performance. In [111] however, the
authors identify the limitation of colour reproduction in virtual make-up
since previous studies focused on the recommendation of the makeup style so
they propose an accurate lipstick colour reproduction method based on CNNs.

For further improvements in virtual makeup systems, in [112] a method to
increase personalisation is proposed by considering a more holistic
recommendation system that goes beyond facial features. By factoring in
classifiers like skin tone, attire, colour, and hair features, this system
offers hyper-personalised makeup recommendations, attuned to individual
preferences and unique physical attributes. In [113], a similar system is
designed but a geometric approach is taken instead based on colour and shape
parameterisation. Similarly, in [114] AR technology has also been utilised in
enhancing makeup creativity where makeup tools that provide tangible
interaction and generate colour on the user have been developed. It also
synchronises with the user’s movement in real time. The study also conducted a
qualitative evaluation to evaluate the users’ satisfaction, which indicated
that by using this system, users can find a suitable makeup style for
themselves and get help increasing their makeup creativity.

The realm of AR in our proposed IoM ecosystem is not just limited to virtual
makeup systems but can also entail the integration of computer vision in
retail scenarios, as seen in [115] which demonstrates the potential of these
technologies in broader applications, such as automatic body measurements for
apparel selection. The versatility of AR and computer vision has been
showcased in diverse applications that are relevant to the IoM, from enhancing
fitness routines through AR technology [116] to the accurate detection and
synthesis of makeup features [117] as well as 3-D tracking of shoes with smart
mirrors [118].

###  III-E Personalised Recommendation Systems

For assistive health and beauty services, recommendation systems are essential
to providing product, treatment and routine suggestions to the user. The first
step towards personalised recommendations in the IoM is the detection and
analysis of user-specific features, this can include skin parameters, facial
feature details, vitals detection or even makeup detection such as in [119],
where deep learning models are used to detect makeup. These models are trained
to recognize subtle changes in the skin, such as the application of makeup,
and can therefore provide a detailed analysis of the user’s current makeup
status. This information forms the basis for personalised makeup
recommendations, highlighting products that complement the user’s current look
or suggesting changes to improve their appearance. These services are offered
by many large cosmetic brands such as a foundation shade finder and
recommender by L’oreal Paris [120], Maybelline [121] and Nars [122].

The results from a detection algorithm can consequently be used to present a
corresponding recommendation like the authors in [123] who detect and classify
eye shapes to give augmented real-time eyeliner style guidance and
recommendations. Most recommendation systems for makeup applications are based
on facial imagery datasets [124] and a corresponding product database. There
is a lack of data from professionals being incorporated into the
recommendation models. This is highlighted in [125], the authors propose a
cognitive knowledge-based system for hair and makeup recommendations that
combines user feature classification with a database of professional
knowledge. The system uses ML algorithms to identify and categorise facial
features, such as eye shape and skin tone. It then matches these features with
professional recommendations to provide personalised makeup and hairstyle
suggestions. The inclusion of professional expertise in this process
significantly enhances the quality of the recommendations, providing users
with advice that aligns with current beauty standards and trends.

Further developments to existing makeup recommendation systems were made
in[126], which proposes a system that offers occasion-oriented makeup
recommendations. By considering the user’s current occasion, the system
provides makeup suggestions that are appropriate for the situation. This
ensures that the recommendations are not only personalised to the user’s
features but are also relevant to their immediate context. The novelty in the
work includes multi-region-wise makeup recommendations, incorporation of
occasion context, real-time makeup previews and continuous makeup feedback.
Additionally, 3D face recognition [127] can also be utilised in recommendation
systems to provide a detailed and accurate representation of the user’s face
[128]. This detailed facial map can be used to provide more precise
recommendations, taking into account the unique contours and features of each
user’s face.

We envision that the integration of recommendation system in the IoM will
include the incorporation of user feedback with deep learning methods [129].
These systems can learn from the user’s past behaviour and preferences,
providing recommendations that align with their user-specific needs. For
example, if a user frequently chooses a specific makeup look, the system will
prioritise similar recommendations in the future. In addition, by
incorporating user feedback, these systems can continually refine and adjust
the recommendations based on the user’s satisfaction with previous
suggestions. This type of corrective learning creates a more responsive and
adaptive recommendation system, leading to a highly personalised and enhanced
user experience. The use of corrective learning approaches can also help
address potential biases in the recommendation system. For instance, biases
may occur when certain products are recommended more often due to higher
ratings or popularity. By incorporating user feedback, the system can learn to
correct these biases, ensuring that recommendations are truly personalised and
not influenced by external factors. Socially aware recommendation systems are
another significant development in the realm of data analytics [130]. Such
systems consider the social context of the user, taking into account their
social network data to provide recommendations. For instance, a socially aware
recommendation system can consider the user’s friends’ product preferences or
their reactions to certain beauty products. This social context can
significantly enhance the recommendation quality, providing suggestions that
are more likely to resonate with the user’s social environment.

###  III-F Wireless Connectivity in IoT

IoT is an integrated system of cutting-edge technology that enables
connections between people, devices, platforms and solutions over the Internet
[1]. There are many connectivity aspects that an IoT system must consider.
Specifically for an IoM connected to other IoT devices, it is essential for
the connectivity of the system to be in accordance with its computational load
and latency requirements.

The specific communication enablers that would be relevant to IoT applications
in assistive health and beauty are dependent on the specific application and
range of the network [131]. This can be seen in the current wireless
communication protocols deployed for different smart environments [132], such
as smart homes which are often connected using the WLAN WiFi due to the short-
range nature of the application. Smart health, on the other hand, also uses
3G, 4G and satellite communications since the applications can be anywhere
from a local hospital setting to a global remote healthcare setting [133]
[134]. Moreover, LPWANs could be attractive for specific IoT applications,
particularly those requiring wide coverage and low power consumption, like
environmental monitoring or asset tracking [135]. Meanwhile, wireless sensor
networks (WSNs) and local area networks (LANs) could benefit more localised
applications, offering high data rates over a smaller area [136].

Furthermore, [137] have classified IoT applications based on end-user type,
either machine-oriented or human-oriented, and based on data rates.
Applications similar to our proposed system include health risk detection
systems which lie in the middle of both end-user type and is on the higher end
of the data rate spectrum with data rates between tens of Mbps to tens of
Gbps. Intelligent shopping applications, which hold similarities to the
requirements of the data analytics in our system, require lower data rates
within the range of 1Mbps to 10Mbps. Despite the lack of academic outlook and
industrial implementation of wireless connectivity in the cosmetics industry,
the analogous retail industry has existing examples of wireless connectivity
being incorporated into their application. A recent example is the
collaboration between Huawei and the Square shopping centre in Camberley to
become the first fully 5G-enabled retail destination in the UK [138]. The
centre covers a 460,000 sq ft area and attracts over 8 million visitors a
year, indicating that the 5G network employed has the potential to support a
system with similar requirements and demands, such as the cosmetics industry
and beauty tech applications.

For larger-scale systems or systems that require large bandwidth and low
latency, 5G wireless communication, especially its millimeter-wave (mmWave)
technology, can address the limitations of previous generations. The mmWave
spectrum offers vast amounts of bandwidth, facilitating high data rates and
acting as a key component of 5G networks [139]. We foresee that for assistive
health and beauty applications related to the proposed IoM such as remote
treatments and remote consultations, data transfer over the 5G network would
be the most feasible since its main characteristics include a mobile access
speed of 1Gbps, a fixed access speed of 1-10 Gbps and a data transmission
delay of 1ms [135]. Another IoT ecosystem, the IoMusT has been evaluated on a
5G-based system designed to support networked music performances (NMPs) in
[140]. The study suggests that while 5G networks hold promise for real-time
interactive systems, like our foreseen IoM, further improvements in terms of
latency and reliability may be needed. Similarly, a future IoM system could be
evaluated on a private 5G deployment, focusing on latency and reliability of
data packet exchanges over the 5G network. Such an evaluation could reveal how
latency increases proportionally with the number of nodes and the presence of
background traffic, and how reliability metrics may remain stable regardless
of conditions.

The advent of the Tactile Internet, combining ultra-low latency with high
availability, reliability, and security, opens up new opportunities for remote
control of devices and could be a crucial enabler for advanced IoM
applications [141] [142]. Given the importance of haptics and wearables in
envisioned IoM applications, the ultra-low latency of the Tactile Internet
becomes crucial. The connectivity requirements of such systems are stringent.
These arise from the necessity for immediate, reliable, and secure data
transmission, especially when handling real-time, haptic-intensive tasks. The
Tactile Internet, with its emphasis on ultra-low latency, can meet these
demands. Thus, the advent of the Tactile Internet could not only enhance the
capabilities of current IoM applications but also pave the way for novel,
haptic-intensive applications in the future.

Furthermore, the convergence of IoT and wireless connectivity is laying the
groundwork for the development of ”Smart Cities”, where numerous connected
devices can interact and exchange data [143]. Such an environment could
provide a more seamless and integrated experience for users of an IoM system,
broadening the possibilities for assistive health and beauty applications.

###  III-G Computational Architectures

Computational architectures and approaches are essential to the realisation of
the IoM, enabling seamless integration and efficient processing of data in
real-time. In particular, edge computing, cloud computing, and distributed
computing are integral to the functioning of IoM, with each offering unique
advantages and capabilities.

  * •

Edge computing involves processing data near its source, reducing latency and
bandwidth usage, and improving real-time interactions [144] [145]. In the
context of IoM, edge computing enables faster processing and analysis of user
data collected by smart mirrors, providing real-time feedback and
recommendations to users. For instance, when a user stands in front of a smart
mirror, the data from the user’s image can be processed immediately at the
edge, enabling the mirror to provide immediate feedback or recommendations.

  * •

Cloud computing provides scalable and flexible computing resources, data
storage, and services over the internet [146][147]. In the IoM, cloud
computing can be used to store and process large amounts of user data
collected by smart mirrors. For instance, cloud-based ML algorithms can
analyse this data to detect patterns and trends, informing personalised
recommendations.

  * •

Distributed computing involves dividing a larger computation problem into
smaller parts that are solved concurrently [148]. Distributed computing
approaches can be used to manage the vast amounts of data generated by smart
mirrors in the IoM. By distributing the computation across multiple nodes,
these systems can process large amounts of data more quickly and efficiently.

One of the challenges for the IoM is to effectively integrate these computing
approaches. The integration of edge, IoT, and cloud computing in a distributed
computing environment can provide a comprehensive solution [149]. The use of
edge computing allows for real-time data processing and interaction, while
cloud computing offers scalable resources and services. Distributed computing
ensures efficient processing of large data sets.

Moreover, the integration of these computing architectures can support
advanced features in the IoM, such as real-time health monitoring, advanced
image processing, and personalised recommendations. For example, edge
computing can support real-time health monitoring by processing vital signs
data immediately. Cloud computing can support advanced image processing and ML
algorithms by providing the necessary computing resources. Distributed
computing can support personalised recommendations by enabling efficient
processing of large user datasets [150] [151].

Table III: Overview of Enabling Technologies and Their Potential for IoM Integration Category |  Technologies |  Applications |  Potential for IoM Integration
---|---|---|---
Skin Imaging Methodologies |  Classification and feature extraction, segmentation, capacitive imaging |  Disease detection, wearable skin sensors |  Early diagnosis, treatment recommendations
Real-Time Health Monitoring |  PPG, Infrared thermography, RF Sensing |  Vitals tracking, disease monitoring |  Continuous monitoring, remote healthcare
Computer Vision |  Augmentation of cosmetics |  Makeup applications, retail scenarios |  Real-time makeup previews, personalised shopping
Personalised Recommendation Systems |  Deep learning, user feedback |  Product suggestions, routine planning |  User-specific recommendations, adaptive learning
Wireless Connectivity in IoT |  5G, LPWAN, WLAN, Tactile Internet |  Wireless data transfer, remote control |  Low latency, high data rates, scalability
Computational Architectures |  Edge Computing, Cloud Computing, Distributed Computing |  Data Processing, Scalable Resources |  Real-time monitoring, Large dataset handling

##  IV IoM Scenarios and Use Cases

We expect that the realisation of the IoM will allow many everyday activities
and aspects of human life to be re-imagined with respect to a technological
ecosystem of interactions between consumers (customers, patients etc.),
professionals (dermatologists, makeup artists etc.) and service providers
(businesses, retail stores etc.). To present an idea and visualisation of what
these interactions could be like and what the implications would be, five
hypothetical scenarios that could potentially arise from the realisation of
the IoM are presented below as well as the corresponding missing capabilities
and considerations. Following on from Fig 4, a visualisation of these
scenarios with the possible interactions is presented in Fig 6.

Figure 6: Visualisation of IoM scenarios and possible interactions

###  IV-A Scenario 1 (Enhanced Beauty Consultations):

Users entering beauty retail stores equipped with IoM devices can approach and
engage with interactive smart mirrors. This mirror, fitted with advanced image
processing and computer vision capabilities, performs an immediate analysis of
the customer’s facial features, skin type, and complexion. Simultaneously, the
smart mirror connects to the customer’s wearable device, such as a smartwatch,
to gather additional data such as skin moisture levels, heart rate, and body
temperature. This data allows the mirror to provide enhanced personalised
recommendations, taking into account the customer’s physiological state. Once
the initial analyses are completed, the customer can select various makeup
products they wish to virtually try on, such as lipstick, eyeliner, or
foundation. The smart mirror in real-time then augments the chosen products on
the customer’s face virtually, enabling them to see how each product
complements their skin without physically applying it. The mirror can then
also suggest products based on the customer’s skin analysis, wearable data,
and their selected preferences. It can propose products that are compatible
with the customer’s skin type, facial features, physiological state, or the
look they aim to create. If the customer is satisfied with the virtual try-on,
they can add the products to their shopping cart directly via the smart mirror
interface. The results from the try-on are stored in the customer’s user
profile, allowing them to access their personalised recommendations and try-on
results from any connected smart mirror in the IoM ecosystem. This feature
also ensures that their future suggestions are more personalised and accurate,
as the system learns from their preferences over time. This scenario extends
to remote situations as well where a user could interact with a beauty
consultant remotely through the smart mirror. The consultant, using the
customer’s facial analysis, wearable data, and virtual try-on results, can
offer personalised advice and product recommendations.

###  IV-B Scenario 2 (Employee Training):

Consider a beauty retail store equipped with IoM devices where a new employee
such as a Makeup Artist (MUA) is beginning their training. The trainee can
engage with the smart mirror, which has been set up for a training session.
The training session commences with the smart mirror displaying a live video
feed of a senior artist demonstrating a makeup technique. As the senior artist
explains and performs each step, the smart mirror overlays AR guides on the
live video feed. These guides assist the trainee by visually emphasising the
crucial parts of the technique, such as the amount of product to use or the
angle and direction of brush strokes. As the trainee replicates the
demonstrated technique, the smart mirror uses its image processing and
computer vision capabilities to analyse their actions in real time. If the
trainee’s application aligns with the demonstrated technique, they receive
positive feedback. However, if the trainee deviates from the technique, the
mirror provides corrective feedback in real time, and the trainee’s smartwatch
can give haptic feedback as an immediate corrective signal. This immediate
feedback helps the trainee to learn and adapt their techniques on the spot,
enhancing learning efficiency. This concept extends to remote situations as
well. Imagine a centralised training session where all the employees of a
beauty brand across the country, or even globally, can participate. The smart
mirror displays a live video feed of the brand’s most senior artist
demonstrating various makeup techniques. The employees can follow along on
their local IoM devices, receiving personalised feedback and haptic
corrections as they practice each technique. After the training, the smart
mirror can administer tests based on the techniques covered during the
session. It evaluates the trainee’s performance using the same real-time
analysis capabilities, grading them based on their precision and adherence to
the demonstrated techniques.

###  IV-C Scenario 3 (Eco-Friendly Inventory Management):

In a beauty retail store equipped with IoM devices where customers can
interact with smart mirrors, every action taken is logged and analysed. This
includes the products they choose to virtually try-on, the products they add
to their shopping cart, the recommendations they accept or reject, and even
the products they opt not to interact with. These interactions provide
extensive data about customer preferences and the performance of different
products. Simultaneously, the smart mirrors are also equipped with sensors
that collect data over time. If a customer purchases a product and uses it
regularly, the smart mirror can track changes in the customer’s skin over
time, providing valuable feedback on the product’s effectiveness and long-term
results. All this data can be aggregated and analysed to identify trends,
preferences, and areas for improvement. Businesses can use these insights to
inform their inventory management, ensuring popular products are adequately
stocked to meet demand and reducing the chances of products being sold out.
Moreover, by predicting product demand more accurately, businesses can reduce
overproduction and wastage, contributing to more sustainable and eco-friendly
practices. They can also optimise their supply chain, ensuring products are
restocked efficiently, further reducing waste and inefficiencies. In remote
settings, customers using smart mirrors at home can have their interactions
and usage patterns logged and analysed. This data can offer businesses a
broader understanding of how customers use their products in their daily
routines, providing valuable insights for inventory planning and product
development.

###  IV-D Scenario 4 (Remote Dermatological Treatment and Skin Health
Analysis):

Users with their skin health tracking wearable and smartwatch at home can be
scanned by their smart mirror to analyse a range of skin parameters such as
skin type, moisture levels, pore size, blemishes, wrinkles, and signs of
ageing. Concurrently, the wearable device and smartwatch measure physiological
data such as heart rate, body temperature, and hydration levels, providing a
comprehensive context to the skin analysis. This combined data from the smart
mirror, wearable device, and smartwatch is then transmitted remotely to a
dermatologist for consultation. The dermatologist can view the detailed skin
and physiological analysis on their end, enabling them to make an informed
diagnosis and suggest personalised treatment plans, ranging from skincare
product recommendations to lifestyle changes and medical treatments, based on
the user’s skin condition and overall health. The smart mirror can also offer
AR tutorials to the user. For instance, if the dermatologist prescribes a
particular skincare routine, the mirror can display a step-by-step AR guide
illustrating how to correctly apply the recommended products. The mirror’s AI
capabilities can monitor the user’s application, providing real-time feedback
to ensure the correct treatment application. If the user applies a product
incorrectly, the smartwatch can provide haptic feedback as an immediate
corrective signal. Post consultation, the smart mirror and wearable devices
continue to monitor the user’s skin health and physiological data. This allows
for tracking the effectiveness of the prescribed treatments, offering valuable
feedback to both the user and the dermatologist. If the mirror or any wearable
device detects significant changes in the skin’s condition or the user’s
health, they can alert the user and the dermatologist through notifications
and haptic feedback, enabling prompt adjustments to the treatment plan.
Through this user feedback, the smart mirrors in the ecosystem can also learn
from each other about different skin types and the effects of different
treatments and products to provide improved recommendations ensuring diversity
and inclusion.

###  IV-E Scenario 5 (All-Inclusive Remote Living Support for the Smart Home):

In a smart home equipped with IoM devices, the smart mirror serves as a
central control and monitoring hub and is integrated with various other smart
devices in the home, such as security cameras, smart locks, thermostats, and
health monitoring devices. The smart mirror is equipped with advanced AI
capabilities that enable it to monitor and control various aspects of the
home. For instance, it can display live feeds from security cameras, alert
homeowners of any detected anomalies, and control smart locks, enhancing the
home’s overall security. The smart mirror can also detect anomalies within the
home, such as a sudden temperature drop or an unusual increase in noise
levels. These anomalies can trigger alerts to the homeowner, allowing them to
take necessary actions promptly. Integrated with the home’s smart devices, the
smart mirror can also control various aspects of the home environment. For
instance, it can adjust the thermostat based on the homeowner’s preferences or
turn off lights in unoccupied rooms, contributing to energy efficiency. In
terms of health monitoring, the smart mirror can connect to wearable devices
to track various health parameters such as heart rate, body temperature, and
sleep patterns. This data can be displayed on the mirror for easy viewing and
tracking over time. If the mirror detects any significant changes or
concerning trends in these health parameters, it can alert the homeowner or
even a healthcare provider. Furthermore, the smart mirror can serve as a
personal assistant, providing reminders for appointments, displaying news
updates, or even providing makeup and skincare recommendations based on the
user’s preferences and skin condition.

### Missing Capabilities

  * •

Scenario 1 illustrates a typical assistive beauty application of the IoM but
it relies heavily on advanced AI, AR, image processing, and computer vision
technologies. As of now, the accuracy of these technologies may not be
sufficient, and there could be discrepancies between the virtual try-on and
the actual result. Moreover, implementing a personalised user profile system
that provides accurate suggestions over time requires sophisticated ML
algorithms and substantial amounts of data, which could pose privacy and
security concerns.

  * •

Scenario 2 relies on seamless real-time integration between the trainee’s
movements and any corrective mechanisms. Another main challenge of this
scenario is accurately detecting and evaluating the trainee’s makeup
techniques using computer vision. The variation in makeup application
techniques can be subtle and complex, making it difficult for the system to
correctly assess the trainee’s performance. Moreover, the system must be
trained with a large amount of data to accurately provide haptic feedback.
This might be challenging due to the complex nature of makeup application
techniques and the need for expert labelling. This scenario can also be
extended to employee or student training for other areas such as dermatology
or dentistry.

  * •

Scenario 3 assumes that users will consistently interact with the smart mirror
and that the data logged will accurately represent their preferences. However,
user behaviour can be complex and unpredictable, and the metrics collected may
not always reflect actual preferences or purchasing behaviour. Additionally,
while the IoM can help reduce waste by predicting demand more accurately, it
cannot control external factors such as supply chain disruptions that can lead
to product shortages or excess. The accuracy of such predictions will rely
heavily on the realisation of smart cities.

  * •

Scenario 4 holds promising potential for remote dermatological treatment by
supporting the diagnostic process, treatment planning and treatment tracking.
This scenario could also be extended to other aesthetic health domains such as
dentistry and optometry. However, it may not be able to replace in-person
consultations entirely because the smart mirror’s analysis might not detect
certain skin conditions that can be observed through a physical examination.
Moreover, data privacy and security are significant concerns, especially when
dealing with sensitive health data.

  * •

Scenario 5 can enhance home security and convenience but it also introduces
potential risks. The integration of multiple smart devices increases the
attack surface for potential cyber threats, raising concerns about data
security and privacy. Furthermore, the system’s reliability is a significant
factor - a system malfunction or false alarm could cause unnecessary distress
or inconvenience for the homeowner. Ensuring the system’s robustness,
security, and reliability is essential for this scenario to be effective.

##  V Current Challenges and Future Research Directions

This section addresses the challenges associated with the development and
realisation of the IoM. It highlights key obstacles including communication
protocol performance, system interoperability, multimodal content
representation and data privacy and security. Possible solutions to these
challenges that can support the practical realisation of IoM systems have also
been presented.

###  V-A Low Latency and High Reliability

  * •

Challenge

Real-time interactions would be fundamental to foreseen IoM applications.
Whether it is streaming a live makeup tutorial with AR overlays, real-time
health monitoring, or providing instant user feedback, low latency and high
reliability are essential. The requirements for these systems bear a close
resemblance to those for real-time gaming or video conferencing, with the need
for high-quality, real-time data streams over both wired and wireless networks
[1].

Achieving low latency and high reliability is a significant engineering
challenge due to the strict requirements of network delay and transmission
reliability. These requirements are set by the expectation of a high-quality
interaction experience. A stable message reception rate and synchronisation
between devices are crucial to maintaining a high level of user interaction
[131]. For envisioned IoM applications, the latency must be on the order of
milliseconds.

Another challenge in communication services is the synchronisation of data
streams produced by devices that do not share the same clock. Even if devices
from different networks initially share the same clock, they require a re-
synchronisation procedure from time to time. Several protocols have been
proposed to achieve such synchronisation, but existing methods are
insufficient as they do not meet ultra low latency requirements [152].

  * •

Future Research Direction

To address this challenge ’physical layer synchronisation’ is an emerging
approach at the wireless communication interface, where the waveform is
designed to carry both data and synchronisation information simultaneously
[153]. Another approach being explored is the optimisation of existing
protocols which involves adapting the communication protocol parameters that
are free to optimise the transmission of data. This has been used to support
various protocols over Bluetooth or to optimise the protocol parameters of Wi-
Fi for various applications [154]. However, these attempts are still not
satisfactory, mostly due to the inherent limitations of the physical layer of
the communication protocol, necessitating further research and development
[153].

The envisioned Tactile Internet is expected to solve these issues by providing
communication networks, both wireless and wired, capable of ensuring ultra-low
latency communications, with end-to-end delays on the order of a few
milliseconds [155]. Along the same lines, the use of edge computing
technologies is expected to play a relevant role in reducing latency and
bandwidth pressure by offloading computation from the cloud to the edge of the
network [148].

The primary data types of the IoM are expected to be image and video meaning
that the demands on bandwidth and latency are even more stringent. The
transmission of high-resolution images and video streams requires significant
bandwidth and low latency to ensure a seamless user experience. This can be
ensured through the development of advanced image and video compression
techniques, as well as efficient coding and transmission protocols.

Interference management is also an important consideration for low latency,
specifically ultra-reliable low-latency communications (URLLC) which is a key
deliverable of 5G networks and beyond 5G networks. Effective interference
management is necessary for meeting the requirements of URLLC and ensuring a
seamless user experience [156]. Many new solutions and insights are being
explored in this area and an integrated research direction focusing on
interconnected IoMs will support the realisation of a dense IoM ecosystem.

###  V-B Interoperability and Standardisation

  * •

Challenge

Interoperability and standardisation are fundamental to the successful
implementation of the IoM. These factors ensure interoperability,
compatibility, reliability, and effective operations on both local and global
scales. Despite their significance, much work remains to be done to realise
fully standardised formats, protocols, and interfaces in the overarching IoT
field to provide more inter-operable systems [1].

In the conceptualised IoM ecosystem, various types of devices are expected to
be utilised for different purposes, both in co-located and remote scenarios.
These devices will be used to generate, track, and interpret multimodal
content, primarily comprised of images and videos. They will need to
dynamically discover and spontaneously interact with heterogeneous computing
resources, physical resources, and digital data. This interconnection poses
specific challenges, including the need for ad-hoc protocols and interchange
formats for image and video data relevant to the IoM, specifically how these
visual data types are encoded, stored, and shared so that different devices
and systems in the IoM can understand and display them correctly. Image and
video data in the IoM could be transmitted over a WLAN using standard
protocols such as Wi-Fi and Bluetooth, which are commonly provided by a
variety of smart devices. However, the challenge lies in establishing a
standard that can facilitate interoperability across heterogeneous devices
without sacrificing flexibility as well as accounting for synchronisation
aspects.

  * •

Future Research Direction

Semantic web technologies are a promising research avenue for addressing
interoperability issues [157]. This would ensure that data is not only
interoperable on a syntactic level but also on a semantic level, meaning that
the significance and context of the data are preserved and understood across
different parts of our ecosystem. This would involve developing appropriate
ontologies to describe the processes mediated by IoM devices with a focus on
real-time applications since these techniques require large segments of data
to make predictions and this can be a challenge in a real-time context.

To address standardisation challenges, future research may focus on the
adaptation of existing multimedia standards, such as those from MPEG, which
provide a solid foundation for managing complex multimedia data. Such a format
would accommodate the multi-modal nature of the IoM, which requires standards
for formats that account not only for the transmission of images and videos
but also for associated metadata. In the context of assistive health and
assistive beauty applications, standardisation plays a pivotal role. Devices
used in these fields, such as health monitors and AR mirrors, need to interact
seamlessly with each other and with other systems. Therefore, the
establishment of standard protocols and interchange formats is crucial. These
would enable these devices to share and interpret data, leading to enhanced
user experiences and more effective operations. Moreover, the nature of health
and beauty applications requires strict data privacy and security standards.
Data transmitted in these applications is often sensitive, and its misuse
could lead to severe repercussions. Therefore, in addition to operational
standards, rigorous data privacy and security standards will also need to be
established.

###  V-C Representation and Analysis of Multimodal Content

  * •

Challenge

Different types of signals are expected to be exchanged within the IoM
including those associated with various sensory modalities, such as image
data, RF signals, environmental data, temperature data, and data from
wearables. They will be dynamically captured through various sensors embedded
in IoM devices, providing varied information about the environment and user
behaviour. For example, image-related information could reveal details about
user interactions with IoM devices or their environment. RF signals could
provide information about device connectivity and communication. Environmental
data could provide insight into the conditions in which IoM devices operate,
and wearable data can provide a wealth of information about the user’s
physiological responses and activities. This multimodal data can be harnessed
to study user behaviour, environmental changes, and the effects of these
factors on the functioning and efficiency of IoM devices. However, the major
challenge the IoM will encounter with varied data types is the development of
systems capable of interpreting multimodal information.

  * •

Future Research Direction

The research direction taken to address the above issue should include the
representation, translation and fusion of multimodal data exchanged in the
envisioned IoM [158]. Where representation involves determining how to
represent and summarise the data, translation or mapping involves determining
how to translate data from one modality to another and fusion involves joining
information from two or more modalities to perform a prediction. A possible
research avenue includes the integration of massive machine-type
communications (mMTC) [159] in the IoM ecosystem to enable efficient,
reliable, and secure exchange of health data wearables and medical data. This
would facilitate not only the real-time transmission of high-resolution skin
images and associated metadata but also the integration of diverse data
streams.

###  V-D Privacy and Security Issues

ii) Security Issues

As with other IoT ecosystems, we predict that the IoM ecosystem will face
substantial challenges related to privacy and security issues. These
challenges are more significant given the diverse range of data types
collected and processed within the IoM such as personal image data, RF
signals, environmental data, and wearable device data. Ensuring the secure and
ethical operation of the IoM requires careful attention to these issues.

Devices within the IoM ecosystem will need to communicate wirelessly and are
therefore exposed to the associated security risks. More specifically, in the
transition to next-generation networks like 6G, the interconnected nature of
theIoM will require embedded security automation and thorough assurance
processes to enforce security policies effectively. Trustworthy systems have
been defined as a key paradigm of the emerging 6G [19], highlighting the
importance of secure and resilient network platforms particularly when dealing
with complex environments. The seamless incorporation of AI and ML into
network systems raises critical issues of transparency and trust. Given the
complexity and non-transparent nature of these technologies, validating their
security is currently limited to output testing against known inputs. To
ensure that AI and ML can be safely integrated into essential services and
infrastructure, there is a need to establish robust methods for assessing AI
security. This involves confirming the confidentiality of data used in AI
processes, preventing the exposure of sensitive information, verifying the
dependability and clarity of AI decision-making, and safeguarding the overall
integrity and resilience of the systems.

A potential direction to ensure security is robust encryption which can be
achieved through various cryptographic techniques. Implementing such robust
encryption presents two primary challenges. Firstly for IoM devices to be
powerful enough to support encryption. and secondly, the use of more
efficient, less energy-consuming cryptographic algorithms [160] to be
deployed. Additionally, blockchain technology can be used to create a
transparent and immutable record of transactions and interactions to add
another layer of security within the IoM ecosystem such as being currently
developed within the IoMusT ecosystem [161]. A uniform security standard will
also need to be established across the IoM framework and industry to ensure
the safety of the data collected by IoM devices.

ii) Privacy Issues

Given the personalised nature of the IoM, there will also be a need to
implement transparent privacy mechanisms across a diverse range of IoM devices
and supporting platforms. Similar to protection from security risks, privacy
in the IoM could be preserved by leveraging encryption and blockchain
technologies, where encryption will protect the data while in transit, and
blockchain will ensure data integrity.

Issues of data ownership must also be addressed to ensure the privacy and data
integrity potential IoM users participating in IoM-enabled activities. IoM
devices could be equipped with machine-readable privacy policies, allowing
devices to check each other’s privacy policies for compatibility before
communicating.

To counteract any data security threats, privacy-preserving computational
approaches can also be integrated into the IoM ecosystem. Since many of the
services of the IoM are enabled by machine learning, alternative approaches
such as federated learning (FL) can be employed to ensure data privacy by
locally training models where the data is generated [162]. This technique also
reduces the load on network resources as well as computational cost. Since the
development of FL is in its early stages, its future research directions can
be aligned and integrated with the IoM to support the large envisioned scale,
network demands and integration of AI in our ecosystem.

##  VI Conclusion

This paper proposed the innovative framework of the IoM which emerges as an
ecosystem under the umbrella of IoT. The IoM envisions an interconnected
network of smart mirrors, which not only reflects the user but also
superimposes layers of digital information and personalised user metrics,
enhancing daily routines for individuals across various demographics and
settings. We have highlighted that beyond the user’s reflection, IoM smart
mirrors can offer many unique services in a single platform such as health
insights, employee training or even inventory management. These capabilities
could profoundly influence personal care routines, fashion choices, health
monitoring, and even mental well-being. The IoM framework has been defined to
be composed of three major elements, smart mirrors, connectivity and
application and services. We have illustrated that the realisation of the IoM
and its elements will be enabled by the integration of many state-of-the-art
technologies such as existing standalone smart mirror technologies, health
monitoring and wireless connectivity. To support this concept and to highlight
future possibilities, we also presented unique envisioned scenarios that could
arise from the IoM by including examples from assistive beauty, assistive
health and assistive living. This was followed by a discussion of the probable
challenges we foresee the IoM ecosystem to face and potential solutions as
well future research directions that can support and be aligned with the IoM
vision. We anticipate that the potential of our conceptualised framework is
vast by bringing a passive everyday object to the forefront of interactive
technology. The IoM concept presents a novel unified platform for not only
assistive beauty and health but also assisted technologies as a whole and
potentially the future vision of smart cities.

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