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#  A Review of the Non-Invasive Techniques for Monitoring Different Aspects of
Sleep

Zawar Hussain [ Macquarie University, Australia  Quan Z. Sheng [ Wei Emma
Zhang [ The University of Adelaide, Australia  Jorge Ortiz [ Rutgers
University, USA  Seyedamin Pouriyeh [ Kennesaw State University, USA

###### Abstract

Quality sleep is very important for a healthy life. Nowadays, many people
around the world are not getting enough sleep which is having negative impacts
on their lifestyles. Studies are being conducted for sleep monitoring and have
now become an important tool for understanding sleep behavior. The gold
standard method for sleep analysis is polysomnography (PSG) conducted in a
clinical environment but this method is both expensive and complex for long-
term use. With the advancements in the field of sensors and the introduction
of off-the-shelf technologies, unobtrusive solutions are becoming common as
alternatives for in-home sleep monitoring. Various solutions have been
proposed using both wearable and non-wearable methods which are cheap and easy
to use for in-home sleep monitoring. In this paper, we present a comprehensive
survey of the latest research works (2015 and after) conducted in various
categories of sleep monitoring including sleep stage classification, sleep
posture recognition, sleep disorders detection, and vital signs monitoring. We
review the latest works done using the non-invasive approach and cover both
wearable and non-wearable methods. We discuss the design approaches and key
attributes of the work presented and provide an extensive analysis based on 10
key factors, to give a comprehensive overview of the recent developments and
trends in all four categories of sleep monitoring. We also present some
publicly available datasets for different categories of sleep monitoring. In
the end, we discuss several open issues and provide future research directions
in the area of sleep monitoring.

###### keywords:

Sleep Monitoring Sleep Stages Sleep Disorders Sleep Postures Vital Signs

##  1 Introduction

Sleep is a vital element in daily human life and is as essential as water and
food. Humans spend one-third of their life in sleep as they need 6-8 hours of
sleep daily for a healthy lifestyle. Quality sleep is also essential. Low
quality or inadequate sleep leads to impaired physical health and may cause
cognitive and psychological problems. People who do not get proper sleep are
more prone to different chronic diseases such as obesity, diabetes, and
hypertension [79]. Spiege et al. [68] showed that inadequate sleep could
increase the risk of obesity and diabetes. Vorona et al. [74] demonstrated
that there is a relation between sleep time and obesity. Brooks et al. [10]
assert that sleep apnea is a high-risk factor for hypertension. Inadequate
sleep is also one reason for road accidents and lower productivity [36, 32].
Due to the importance of sleep, this area has become very popular among the
research community. Many studies have been conducted to analyze and understand
the quality and behaviour of sleep. It has become a separate branch of
medicine and has gained a lot of attention recently [78, 27].

There are various sub-topics in the area of sleep monitoring. Different
studies have focused on one or more of these sub-topics. Some studies focus on
sleep stages classification, and they are more interested in investigating the
sleep cycles [9, 1]. Sleep cycles are fundamental, and any anomaly in sleep
cycles or less time in a specific cycle can lead to disorders causing health
problems. Some studies focus on monitoring different postures during sleep
[13]. They investigate the body movements and postures such as supine, prone,
left, and right and the time spent in each pose during sleep. Posture
monitoring can provide useful information about sleep behaviour and is vital
for preventing pressure ulcers and sleep apnea [45]. Other studies focus on
the detection of sleep disorders [52]. These studies investigate sleep-related
disorders such as apnea, insomnia, and restless leg syndrome. The detection of
these disorders is very challenging as the patients are unaware of these
disorders. One other group of studies focuses on monitoring vital signs such
as respiration and heart rate during sleep [27]. Vital signs are correlated
with sleep behavior and can provide important information about sleep quality
and some disorders.

From the related literature, we can find that various techniques have been
used to monitor sleep. The traditional approach to analyze and understand
sleep quality is to use clinical methods [14]. Clinical methods apply
specialized hardware and environment and are supervised by trained people
usually, the health workers. The patient has to stay overnight in the clinic.
This approach is both complex and costly. Nowadays, smart health technologies
offer ubiquitous solutions for sleep monitoring, which are relatively cheap
and easy to use [53, 55]. Innovations in intelligent sensing have led to the
development of many sensors that can be used as wearable or embedded in bed
frames. These sensors can easily monitor sleep by capturing the related data.
These solutions can be used for in-home sleep monitoring and do not require
trained technicians.

In this paper, we review and analyze non-invasive and in-home studies
conducted for monitoring sleep. We focus on techniques which do not require
the users to wear different types of invasive sensors which can cause
disturbance in natural sleep or require special environment. We divide the
related literature into four categories: sleep stages, sleep postures, sleep
disorders, and vital signs monitoring. We review the state-of-the-art works in
these fields and provide the reader an overview of the advancements in these
areas of sleep monitoring. We discuss and compare both wearable and non-
wearable (device-free) methods used for monitoring sleep and provide a
comprehensive comparison of these techniques using key factors. We also
identify the possible research gaps and provide future research directions for
the researchers.

The rest of the paper is organized as follows. Section 2 presents the related
work, and Section 3 provides the background of sleep monitoring and the
clinical approaches used for sleep monitoring. We also collect and present
some publicly available data sets in this section. Section 4 presents the four
categories of sleep monitoring in detail. We review the latest literature for
both wearable and non-wearable solutions in this section. Section 5 provides
future research directions and Section 6 offers some concluding remarks.

##  2 Related Work

There are several surveys that summarize the works done in sleep monitoring
and focus on different aspects of sleep monitoring. Kelly et al. [37]
presented a review article summarizing the recent developments for in-home
sleep-monitoring devices. This study mainly focuses on the commercially
available devices and categorizes them in brain signal-based, autonomic
signal-based, movement-based, and bed-based systems. This work also presents
the discussion about using other consumer devices that can be used for sleep
monitoring. The authors highlight research gaps in the sleep monitoring area
and concluded that portable devices are increasing for sleep monitoring.
Still, these devices and systems need to be standardized. Surantha et al. [70]
conducted a survey on the Internet of Things (IoT) related works for sleep
monitoring. The emphasis of this work is on the quality of sleep. The authors
discussed a few sleep-related disorders and the clinical techniques for sleep
monitoring. This work presents an IoT architecture for sleep quality
monitoring. Ibanez et al. [29] focused on sleep questionnaires and diaries.
This survey only reviews the self-reporting approaches for sleep assessment
and provides some critical analysis of these techniques’ accuracy and
validation.

Matar et al. [46] presented a comprehensive review of the unobtrusive methods
used for sleep monitoring. This survey addresses three main topics: sleep
stages, sleep disorders, and monitoring techniques. The authors categorized
the related literature based on physiological signals captured during sleep,
such as breathing, cardiac signals, and body movements, and review the various
studies in each of these categories. The authors also discussed the challenges
and limitations of the techniques presented for each category. Sadek et al.
[59] presented a survey on the use of non-intrusive technologies for sleep
monitoring. The paper discusses both wearable (e.g., bracelets) and non-
wearable (e.g., bed-embedded) approaches but the main focus of this work is
consumer devices and various gadgets available in the market for sleep
assessment. There is very less content on the research aspect of the discussed
technologies. Fallmann et al. [14] proposed a survey on sleep monitoring
techniques. This survey discusses sleep behaviour in terms of movements,
stable state and disorders. The authors categorize the literature into
clinical and home-based approaches and summarize different techniques from
each category. However, the main focus of this survey is computational
analysis methods for sleep assessment. The authors discussed various
computational studies presented for sleep stages classification, body
movements monitoring, and disorders detection and list the challenges and
limitation of those techniques.

Tran et al. [72] presented a survey on the Doppler radar-based monitoring
techniques for the detection of sleep apnea. This paper describes the
architecture of radar-based system and discusses the different aspects of the
system such as noise removal and signal processing. The authors categorized
the radar-based techniques into four categories based on their methodology.
These categories are time-frequency analysis which uses time-series and
frequency domain in the signal processing, numerical analysis which uses
numerical techniques, classification and training which uses machine learning,
and other methodologies which uses experimental and mathematical modeling.
Ahmadzadeh et al. [3] conducted a survey on biomedical sensors, technologies
and algorithms for the breath-related sleeping disorders. The paper
categorizes the literature into invasive and non-invasive techniques and
provides a review of the related articles in both the categories. The authors
also discuss the different challenges involved in the sensors selection, data
processing, and classification algorithm used in the presented literature. Pan
et al. [51] presented a review of the current techniques and future challenges
in the area of sleep monitoring. The authors reviewed the related literature
on sleep stages classification and sleep disorders and address some of the
challenges faced by the community in the current systems for sleep monitoring.
The paper also discusses clinical and wearable sensor-based approaches for
sleep monitoring and proposed their own system for sleep monitoring. The
proposed system consists of various wearable sensors attached to both wrists,
both legs and chest position and uses machine learning-based approach to
monitor sleep stages, body movements, and snoring.

Surveys Year Main Focus Data sets Comparisons Sleep Stages Sleep Disorders
Vital Signs Postures Clinical Methods In-Home Methods Wearable Non-wearable
[37] 2012 Consumer devices No No Yes No No No No Yes Yes [70] 2016 Sleep
quality No No No Yes No No Yes No No [29] 2018 Questionnaires and diaries No
Yes No Yes No No No No No [46] 2018 Physiological signals (actigraphy) No Yes
Yes Yes Yes Yes Yes Yes Yes [59] 2019 Consumer devices No No No No No No No
Yes Yes [14] 2019 Computational analysis methods No Yes Yes Yes No Yes Yes Yes
Yes [72] 2019 Sleep apnea No No No Yes No No No No Yes [3] 2020 Sleep apnea No
Yes No Yes Yes No Yes Yes Yes [51] 2020 Wireless body area network No Yes Yes
Yes Yes No Yes Yes Yes Our Survey 2021 Ubiquitous methods Yes Yes Yes Yes Yes
Yes Yes Yes Yes

Table 1: Summary of the previous surveys and this survey

There is a growing interest in the area of sleep monitoring and several
surveys have been conducted recently. Table 1 gives a summary of these
surveys. As can be seen from the table, these surveys focus on different
aspects of sleep monitoring like disorders detection, sleep stages
classification or posture recognition during the sleep. Also, the techniques
reviewed by these surveys are different and focus on one or more aspects of
sleep monitoring. None of these surveys discussed the datasets for sleep
monitoring which is a very important part of research in this field. To the
best of our knowledge, there is no survey paper which provides a complete
review of the work conducted in all the four sub-categories of sleep
monitoring using the non-invasive approach. Due to this reason, we believe
that there is a need for a comprehensive review to give readers an overview of
the latest research efforts in all the sub-categories of sleep monitoring,
especially using the unobtrusive techniques. In this paper, we review the
recent works in sleep monitoring in the four sub-categories of sleep
monitoring which are sleep stages classification, sleep disorders detection,
posture recognition and vital signs monitoring.

##  3 Background

In this section, we provide the background of sleep monitoring. We also
discuss the clinical methods used for sleep monitoring. Due to the well-
recognized importance of quality sleep and high prevalence of inadequate
sleep, sleep monitoring has become a very important area of research. Many
studies have been conducted which proposed different techniques for monitoring
different aspects of sleep.

Figure 1: General architecture of sleep monitoring system.

The general architecture of a sleep monitoring system can be divided into four
layers as shown in Figure 1, consisting 1) sensing, 2) observed parameters, 3)
processing, and 4) application. The first layer deals with the choice of
sensing method. Different sensors have been used in the literature to capture
the information about the sleeping. In clinical methods, various sensors are
used in an invasive method to capture the physiological signal while the
subject is asleep. In-home methods use both wearable and bed-embedded devices.
In the wearable method, different sensors are worn by the subjects on their
body at different positions such as wrist, leg, ankle, chest and neck. The
sensors (e.g., pressure, capacitive, RFID tags) can be embedded in the bed
frame or the sleeping mattress as well. The second layer of the architecture
deals with the type of parameters observed. The sleeping involves various
factors such as body movements, hand movements, leg movements, breath rate,
heart rate and brain signals. Based on the objective of the system, one or
more of these parameters can be captured. The third layer consists of the
processing of the data captured. Two main approaches are used in the
literature for processing the data. These approaches are machine learning and
signal processing. The final layer consists of application category in which
the final output of the system is decided. The output can be one or more of
the given four categories.

###  3.1 Clinical Approaches for Sleep Monitoring

Clinical methods refer to those methods which are conducted in special
environments (labs/clinics/hospitals) under the supervision of trained staff.
Currently, these methods have the highest accuracy and are considered as
standards for sleep monitoring. These methods can be termed as invasive
because they involve various sensors attached to different body parts of the
subject (e.g., head, nose, fingers, toes, and chest). These sensors cause
disturbance in the natural sleep and cannot be used for long term monitoring.
In this section, we discuss some of the important clinical methods used for
sleep monitoring.

####  3.1.1 Polysomnography

The current gold standard technique used for sleep analysis and diagnosis of
sleep disorders is Polysomnography (PSG) [62]. PSG is a complex clinical
process which requires the subject to sleep for hours wearing many sensors as
shown in Figure 2. This procedure can be conducted in specialized environment
under the supervision of trained people (sleep experts and technicians). The
patient has to stay overnight and the data from electroencephalogram (EEG),
electrocardiography (EOG), electromyography (EMG), nasal airflow, and pulse
oximetry is recorded via the attached sensors. The data is then processed to
analyze the sleep behaviour. PSG is not an ideal procedure for sleep analysis
as the subject has to wear many different kinds of sensors and has to stay
overnight in the specialized environment. This procedure is time-consuming,
complex, and expensive and cannot be used for long-term [17].

Figure 2: Polysomnography222Source: https://www.sleep-apnea-
guide.com/polysomnogram.html

####  3.1.2 Actigraphy

Actigraphy is another approach which has gained popularity in health care
solutions [67]. Actigraphy is widely accepted as standard in medical domain
for objective sleep quality measurement [14]. In this approach, a device which
mainly consists of an accelerometer (can also use other inertial sensors such
as gyrsocope or magnetometer) is attached to the body (preferably wrist or
ankle position) of the person which can record all the physical movements of
the person. The normal assessment consists of seven consecutive days to get a
representative image of the user’s sleep. The data is recorded and then
processed using machine learning or signal processing techniques for sleep
analysis. This method has the advantage that it can monitor the person’s sleep
for longer period without any extra cost or effort. However, it can only
provide information about the wake and sleep activity labels and the physical
movements during the sleep [63].

####  3.1.3 Ballistocardiography

Ballistocardiography (BCG) is another clinical technique which is based on the
signal generated by the heart when it pushes the blood into the vessels. This
is a very old technique but recently got significant importance and has been
used for vital signs and sleep monitoring [20]. This approach provides non-
invasive monitoring of cardiac signals and does not require the users to wear
any sensors on their bodies. Different sensors are employed to capture BCG
signals such as fiber brag grating sensors, electromechanical film sensors,
piezoelectric film sensors, microbend fiber optic sensors, polyvinylidene-
fluoride sensors, hydraulic and pneumatic sensors, and pressure sensors
[31][39]. These sensors can be embedded in ambient environment such as bed and
chairs. BCG method is mostly used for monitoring the cardiac activity and can
provide good results but it involves a lot of sensors to be deployed which
makes it costly. Also, these sensors can be affected by different factors such
as bed frames, mattress thickness, body movements, bed-partners and motion
artifacts.

###  3.2 Publicly Available Data Sets for Sleep Monitoring

Nowadays, there are many data sets publicly available in various research
areas. These data sets are playing a vital role in the advancement of research
as the research community can use these data sets to evaluate their newly
proposed solutions. Like other fields of science, there are publicly available
data sets for sleep monitoring as well. Most of these data sets consist of PSG
recordings and are collected in specialized environment (hospitals/clinics).
The National Sleep Research Resource (NSRR)333Source:
https://sleepdata.org/datasetsoffers free access to many sleep related data-
sets. Table 2 provides details about some of the publicly available data sets
for sleep monitoring.

Data set Year Focus Sensor Used Deployment # Subjects Sample Size Age range
[57] 1997 OSA,Sleep stages PSG Specialized environment 6000 NA 40 [21] 2000
General purpose PSG Specialized clinic 16 8-10 hours per subject 32-56 [54]
2000 Apnea ECG Specialized clinic 32 34313 minutes NA [23] 2011 Apnea, sleep
stages PSG (Jaeger-Toennies system) Specialized clinic 25 5.9 to 7.7 hours per
subject 28-68 [50] 2014 General purpose PSG Specialized clinic 200 One full
night per subject 18-76 [38] 2016 General purpose PSG Specialized clinic 100 +
8 + 8 1, 2, and 1 session for each subject NA [56] 2017 Sleep postures
Pressure sensor (Vista Medical FSA SoftFlex 2048) Sleeping mat under mattress
13 23400 19-34 [89] 2017 Sleep stages Radio Room 255 90000 (30-sec epoch) NA
[19] 2018 Arousal PSG Specialized clinic 994 1983 55 (mean) [24] 2018
Respiration RF (UWB-IR,WiFi CSI, Zigbee RSS, and sub-1 dB quantized RSS)
Either sides of the bed 20 160 hours 55-60

Table 2: Summary of the publicly available data sets

##  4 Sleep Monitoring

There are four categories of sleep monitoring (see Figure 1). In this section,
we review the latest work conducted in these categories.

###  4.1 Sleep Stages Monitoring

A very important aspect in sleep monitoring is the classification of sleep
stages which can help in the analysis of the sleep behaviour and diagnosis of
sleep disorders. Initially, the sleep was divided into six stages by the R-K
method proposed by Rechtschaffen and Kales in 1968 [58]. Later in 2007, the
American Academy of Sleep Medicine (AASM) [30] updated their manual for sleep
scoring and divided the sleep into five stages. These stages are wake, rapid
eye movement (REM) and three stages (N1, N2, and N3) of non-rapid eye movement
(NREM) as shown in Table 3. Sleep cycle in healthy adults last for around 90
to 100 minutes and it starts with N1 stage which is the shortest followed by
N2 (which lasts for around 10 to 25 minutes) and then N3 stage. After N3, the
REM stage counts for 20-25 % of the total sleep. Monitoring and identification
of these sleep stages is very critical for diagnosis of different sleep
disorders and it can also help in tracking the treatment’s response in
patients [89]. In this section, we review the work conducted for sleep stages
classification using the ubiquitous approach.

####  4.1.1 Wearable-based Approaches for Sleep Stage Classification

Due to the advancements in the production of wearable devices, the use of
wearables has been increased significantly. Many applications especially,
human activity recognition [43, 28] and healthcare [2] are now adopting the
wearable approach because of the low-cost and non-complex operation. In this
section, we review the wearable-based techniques used for sleep stage
classification.

Stage Description Wake W The person is awake but on the bed NREM N1 Dosing off
which lasts for 1 to 5 minutes N2 A more subdued state in which the body is
relaxed. The body temperature drops and the breathing, heart rate, and brain
activity starts to slow down. N2 can last for 10-25 minutes N3 Deep sleep.
Muscles are relaxed and breathing and brain activity slow down. N3 can last
for 20-40 minutes and is the most important part of the sleep for different
bodily processes. Rapid Eye Movement (REM) REM In this stage, the whole body
is paralyzed except the eyes and the brain. Dreams occur at this stage. REM is
responsible for around 25% of the total sleep in adults.

Table 3: Five stages of sleep

Beattie et al. [6] conducted a study for sleep stage estimation using the
optical pulse plethysmography (PPG) and accelerometer. They collected the
overnight sleep data from 60 adults who wore the devices (Fitbit) on both of
their wrists. The authors use the Embletta for validation (ground truth)
purposes which collects the EEG, ECG and EOG signals. Two sleep experts were
hired to score the PSG records from the Embletta device as per the AASM
guidelines. The labeling from the experts is used as the ground-truth for the
valuation of the proposed approach. This work uses three sets of features
which are from movement, heart rate and respiration. These feature are fed
into a linear discriminant classifier which can predict one of the four
classes (Wake, REM, Light, and Deep) with 69% accuracy. One of the main
findings of this study is that actigrapghy can provide some information about
the sleep stages (such as awake and sleep) but cannot accurately predict all
the stages. By adding the physiological data (heart rate and breath rate)
captured by PPG, the sleep stages can be detected with reasonable accuracy.
Zhang et al. [87] proposed a techniques for sleep stage classification based
on actigraphy and heart rate captured by a wearable device. To get the ground
truth, five sleep experts analyzed the PSG data and assigned the five sleep
stages independently. The low-level features in both time and frequency domain
are extracted from accelerometer and heart rate data which are then combined
to learn the mid-level features. A bidirectional long short-term memory
(BLSTM) architecture is trained on the final features to classify the sleep
data into one of the five stages. The authors evaluate the performance of the
proposed system for two different groups; the resting group went to sleep
without doing any exercise while the comprehensive group exercised before
going to bed. The achieved an F1 score of 64%for the resting group and 60.5%
for the comprehensive.

Boe et al. [8] presented a technique based on wearable wireless senors to
detect different stages of the sleep. This work uses IMU devices attached to
different positions on the body and collects the physiological data including
the body temperature during the sleep. PSG is used to get the ground truth. A
population-based bagging classifier with decision tree (DT) is used to
classify the sleep data into different classes. This work has evaluated the
proposed approach for two-stage (wake vs sleep), thee-stage (wake vs NREM vs
REM) and four-stages classification (wake vs light vs deep vs REM) and the
performance was compared with that of Actiwatch. The results show that their
approach outperforms the Actiwatch but the performance of the proposed
technique is poor for the four-stage classification. Zhang et al. [88]
designed their own wearable multi-sensor vest called SensEcho for sleep stage
classification. The SensEcho uses two electrodes attached to the chest and
abdomen region and one oximeter attached at wrist position. An accelerometer
is also embedded in the vest. This multi-sensor vest captures the ECG and the
breathing signal. A number of different features including both time and
frequency domain features are extracted form the raw data. The extracted
features are fed into a BLSTM which can assign one of the four stages with
80.75% accuracy. The authors also evaluated the proposed approach on a
publicly available data set and achieved the same accuracy. Nakamura et al.
[48] proposed another wearable technique called Hearables which uses an in-ear
sensor for sleep stages classification. This in-ear device has two electrodes
and can easily record the ECG signal for the over-night sleep. After the pre-
processing, different features are extracted which includes structural
complexity features and spectral features. The final features are fed into an
SVM with radial basis function (RBF) kernel which classify the sleeping data
into five classes with 74% acuracy. PSG is used as a ground truth.

####  4.1.2 Non-Wearable-based Approaches for Sleep Stage Classification

A major issue with the wearable approach is that users are bound to wear the
device all the time. Nowadays, many solutions are using the device-free (non-
wearable) approach [28] in which the users are not required to wear anything.
Non-wearable devices-based approaches have the advantage of having the least
disturbance in natural sleeping because the users are not required to wear any
sensor. The sensors devices are either placed in the environment (room) or
embedded in the bed frame or mattress. Sleep monitoring can be done via a
single device such as radar or multiple sensors can be integrated in the bed.
The most commonly used sensors which are used as non-wearable for sleep
monitoring include pressure sensor, radar, Wi-Fi, radio, load cells, force
sensors, smart phones etc. In this section, we discuss the non-wearable-based
techniques used for sleep stage classifications.

Zhao et al. [89] proposed a radio signal-based technique for predicting the
sleep stages without attaching anything to the subject body. This technique
uses a radio device placed in the bedroom while the user is sleeping and then
analyzes the reflected signals to extract the information about the sleep. One
of the main contribution of this work is the feature engineering. The authors
use game theory and adversarial training for discarding all the irrelevant
information in the reflected signal and extract only the relevant features.
This work uses a combination of Convolutional Neural Network-Regular Neural
Network (CNN-RNN) in their model architecture and achieves an accuracy of
79.80%. Zhang et al. [86] presented a non-contact technique for sleep stage
classification using the Doppler radar. A continuous-wave Doppler radar is
placed near the user bed which emits a single tone signal. The reflected
signal contains the information about the breathing of the user because the
chest movements induce disturbances in the signal. The reflected signal also
captures the movements of the user. Different features related to body
movements, respiration rate and heart beat are extracted from the reflected
signal which are fed into a bagged tree classifier. The classifier can assign
one of the four classes with 78.6% accuracy.

Yi et al. [83] proposed the use of a hydraulic sensors for monitoring the
sleep stages in a non-invasive way. The hydraulic sensors are embedded in the
bed under the mattress which can capture the body movements as well as the
movements caused by breathing and BCG. The captured data is first cleaned by
passing through Butterworth filter to remove the low and high frequency noise.
Different features including both time and frequency domain are calculated for
respiration and heart rate. Two classifiers, Support Vector Machine (SVM) and
k-nearest neighbors (KNN), are used in both single layer and hierarchical
configurations to predict the three stages of sleep. Single layer SVM achieved
the highest accuracy of 85.5%. A similar approach is presented by Gargees et
al. [18] which also uses the hydraulic sensors embedded in the bed frame to
classify the different sleep stages. After cleaning the BCG data captured by
the hydraulic sensors, it is passed through a deep model architecture which
consists of CNNs and LSTMs. A pre-trained CCN (trained on the sleep data of 56
subjects from another study) is employed using the transfer learning
technique. The study shows that transfer learning can boost the performance of
the classifier and can achieve an accuracy of 90.80%. Zhang et al. [85]
presented the design and implementation of a system called SMARS which uses
ambient radio signals for sleep stage classification. The basic working
principle of this system is the tiny changes induced in the reflected signal
due to breathing and body movements. This work employs a statistical model
which accounts for all the reflecting and scattering multi-paths to accurately
estimate the breath rate and hence the sleep stages. Awake and sleep stages
are detected based on the frequency of movement i.e., frequent movements means
the person is awake. For the classification of REM and NREM stages, an SVM
with RBF kernel is used which can predict the two classes with 88% accuracy.

Work # Stages Sensor-Placement Device Used Methods Used Accuracy Obtrusiveness
Cost Parameter Observed # Subjects Validation Method [6] 2017 4
Accerelerometer, PPG-wrist Fitbit Linear discriminant classifier 69% Low Low
Movement, breathing, heart rate 60 PSG by Embletta MPR [87] 2018 5
Accelerometer, PPG- wrist Microsoft Band I BLSTM-based RNN 60.50% Low Low
Movement, heart rate 39 PSG by Compumedics. [8] 2019 2,3,4 IMU, Temperature-
chest, wrist, ankles and forehead BioStampRC, Thermochron iButton, ActiWatch
Spectrum (for comparison) Bagging classifier with DT 60.60% Medium Medium
Movement, temprature,ECG 11 PSG [88] 2019 4 Accerelerometer, ECG-chest,
oximeter-wrist SenseEcho BLSTM-based RNN 80.75% Medium Medium Breathing, ECG
32 PSG [48] 2020 5 EEG-ear Self-made SVM with RBF kernel 74.10% Low Medium ECG
22 PSG by SOMNO screen

Table 4: Comparison of the wearable-based solutions for sleep stage
classification

Work # Stages Sensor-Placement Device Used Methods Used Accuracy Obtrusiveness
Cost Parameter Observed # Subjects Validation Method [89] 2017 4 Radio-room
COTS Game theory, CCN, RNN, adversarial training 79.80% Low Low Breathing,
heartbeat from reflected signal 25 EEG-based sleep monitor [86] 2017 4 Radar-
room Digital-IF Doppler radar Bagged tree 78.60% Low Low Movement,
respiration, heartbeat from reflected signal 1 PSG [83] 2019 3 Pressure
sensor-bed Hydraulic bed system SVM with cubic kernel, KNN 85% Low Medium
Respiration, heartbeat 5 PSG [18] 2019 3 BCG, bed Hydraulic bed system
Transfer learning, CNN, LSTM, DNN 90.80% Low Medium Respiration, heart rate 5
PSG [85] 2021 3 Radio-room Atheros WiFi chipsets. SVM with RBF, Statistical
model 88% Low Low Movement, respiration from refelcted signal 6 PSG, EMFIT,
ResMed

Table 5: Comparison of the non-wearable-based solutions for sleep stage
classification

#### Summary

A summary of the work presented for sleep stage classification is given in
Table 4 (wearable-based) and Table 5 (non-wearable-based). From the presented
work, we can see that solutions with higher level of obtrusiveness and cost
have the better accuracy. Also, these systems can recognize more stages than
solutions with low level of obtrusiveness. This can be explained by the level
of physiological signal captured by these complex systems. Another observation
is the increasing use of machine learning and feature engineering in this
area. More solutions are now focusing on the feature engineering aspect and
then use the existing models. Accelerometer is the widely used sensor in
wearable solutions while non-wearable solutions are mostly using the pressure
sensors. Although, the wearable systems are easy to use, the accuracy is low.
More work is required to increase the accuracy of these solutions to an
acceptable level.

###  4.2 Sleep Posture Monitoring

Recognizing sleep postures is a very important factor in the assessment of
sleep. It provides very useful information about the quality of sleep. Posture
recognition is of particular importance for bedridden patients to avoid
pressure ulcers also called pressure sores. Prolonged stay for elder people
and post-surgery patients in the bed can cause skin problems due to laying in
one posture for longer time. Monitoring the postures can help the care givers
to provide better support and avoid the pressure ulcers and other skin
problems. Due to its importance, many studies have been conducted to monitor
the sleeping postures. In this section, we review some of the recent works
done for posture recognition.

####  4.2.1 Wearable-Based Approaches for Posture Monitoring

In this section, we discuss the wearable-based techniques used for sleep
posture monitoring. Sun et al. [69] presented a sleep monitoring system called
SleepMonitor by using the accelerometer embedded in a smartwatch. The working
of this technique is based on the idea that wrist position is correlated with
the body postures. SLeepMonitor uses the wrist positions to find the sleep
postures. Different statistical features are extracted from the accelerometer
data which are used to train four different classifiers including NB, BN,DT,
and RF. The RF performs better than the other models and recognizes the four
sleep postures with 91.70% accuracy. Fallmann et al. [15] also used the
accelerometer-based approach for sleep posture recognition but this work
attaches the senors to both ankles and the chest. This work uses Matrix
Learning Vector Quantization (GMLVQ) model, which is a distance-based
classifier, to recognize one of the eight classes.

SleepGuard [11] also uses an IMU embedded in the smartwatch to monitor
different sleep related activities including the postures. SleepGuard uses the
arm position to recognize different sleep postures. In addition to recognize
the four basic sleep postures, SleepGuard can also recognize three hand
positions. Moreover, the proposed system can monitor other important sleep
factors such as body rollover, micro movements, acoustic events (snoring,
coughing etc.), and illumination condition. The work conducted by Jeon et al.
[34] uses wearable devices attached to both wrists and the chest. The wearable
device consists of IMU sensors. A two-level classifier based on Dynamic State
Transition (DST)-framework is used to classify the sleep postures and motions.
First, the data is classified into Sleep Posture Change (SPC) and NonSPC
groups using Random Under Sampling Boost (RUSBoost) algorithm. In the second
level, SVM and RF algorithms are used to further classify SPC into eight and
NonSPC into four motions. iSleePost [33] is another non-invasive system which
uses inertial sensors attached to the wrist and chest positions of the users.
The chest-attached sensor is only used in the training phase and is no longer
needed after the model is trained. Basic features are extracted form the
accelerometer data and two machine learning models (SVN and RF) are used to
detect the four sleeping postures.

####  4.2.2 Non-Wearable-Based Approaches for Posture Monitoring

In this section, we present the non-wearable-based techniques used for sleep
posture monitoring. Xu et al. [80] presented a matching-based approach called
Body-Earth Mover’s Distance (BEMD) for sleep posture recognition using the
pressure image captured by the pressure sensors embedded in the bed sheet.
BEMD treats the pressure image as 2D weighted image and calculates three
descriptors (planar, polar and projection) before applying the histogram
normalization. Euclidean distance and EMD are combined to evaluate the
similarity of sleep postures. Finally, a skew-based posture classifier is used
to classify the postures in one of the six postures. The skew-based classifier
consists of KNN and skew rate classifier. Barsocchi et al. [5] presented an
unobtrusive system for posture recognition using the force sensing resistors
(FSR) embedded in the bed frame. FSR captures the pressure caused by the human
body (when lying in the bed). A multi-class logistic regression model is
trained to classify the pressure data into one of the four postures.

Hu et al. [26] proposed an RFID-based system to recognize sleep postures. This
work attaches multiple (8x8) passive RFID tags to the blanket which can
capture the posture information of the sleeping person. When the person moves
or change sides in the bed, the RSSI of the received signal at the reader will
be disturb accordingly. This work exploits the changes caused in the received
signal as a result of the posture changes and apply CNN after removing the
noise. The CNN can divide RSSI image (each pixel corresponds to a tag value)
into one of the five postures with 86% accuracy. Liu et al. [42] proposed a
similar approach called TagSheet in which they embed multiple passive RFID
tags into the sleeping mat/bed sheet in the form of a matrix. The system takes
grey-scale snapshots of the variance in the RF signal from all the tags which
can represent the different postures. This work employs various image
processing techniques such as Gaussian blur, Ostu-based binary conversion of
the grey-scale image and the removal of scattered pixels. TagSheet then
applies multi-level hierarchical recognition by first classifying the postures
into three coarse-grained classes and then refine it by further classifying to
get the total six classes. One of the advantage of this system is that no
prior data or training is required and the system can be used in a plug and
play manner.

Yue et al. [84] conducted a study exploring the wireless signals and proposed
a system called BodyCompass for sleep posture monitoring in a home
environment. BodyCompass uses an FMCW radio which transmits a low power
signal. These signals are reflected from the environment as well as the person
body. BodyCompass analyzes these reflected signals by using different signal
processing and machine learning techniques to infer the sleep postures. This
work calculates the sleep postures in terms of angle, i.e., the angle between
the bed surface and the trunk of the sleeping person. If the angle is 0, it
means the posture is facing upward while 90 angle means posture is facing
rightwards. BodyCompass first extracts filtered multi-path features specific
to a person from the RF reflection. A fully connected neural network is
trained on the data of a single user in a specific home. Then transfer
learning is used to train the model for predicting the posture of new users.
The use of transfer learning reduces the requirement of training data for new
users. BodyCompass is evaluated in various settings and achieves an accuracy
of around 94%.

Work # Posture Sensor-Placement Device Used Methods Used Accuracy
Obtrusiveness Cost Parameter Observed # Subjects Validation Method [69] 2017 4
Accelerometer- wrist Sony Smartwatch 3, Huawei Watch NB, BN, DT, RF 91.70% Low
Low Wrist position 16 Smartwatch attached at belly, Comparison with BioWatch
[15] 2017 8 Accelerometer-legs, chest Shimmer3 Generalized Matrix Learning
Vector Quantization 98% Medium Low Body position 6 Video camera [11] 2018 4+3
Accelerometer, gyroscope, orientation sensor-wrist Self-made Euclidean
distance, template matching 90% Low Low Arm position 15 Video camera [35] 2019
4+8 Accelerometer, gyroscope, magnetometer-both wrists and chest AHRS
EBIMU24GV Directional features, two-level classification, Random Under
Sampling Boost, SVM, RF 95% Medium Low Body position 5+11 Kinect [33] 2021 4
Accelerometer- wrist, chest Koala SVM, RF 85% Medium Low Body position 1
Accelerometer

Table 6: Comparison of the wearable-based solutions for sleep posture
recognition

Work # Posture Sensor-Placement Device Used Methods Used Accuracy
Obtrusiveness Cost Parameter Observed # Subjects Validation Method [80] 2016 6
Pressure sensor-bedsheet Piezo-electrical pressure sensors EMD, Euclidian
distance, KNN, Skew rate classifier 91.21% Low Medium Pressure image 14 NA [5]
2016 4 Force sesning resistors-bed Self-made Logistic regression 91.20% Low
Medium Pressure values 3 Video [26] 2018 4 RFID tags-blanket COTS CNN 86.24%
Low Low Breathing from the reflected signal 1 NA [42] 2019 6 RFID tags-
bedsheet Impinj H47 Image processing, polinomial fitting, hierarchical
classification, PCA 96.70% Low Low Breathing from the reflected signal 12
Video [84] 2020 NA Radio-room FMCW radio Filtered multipath extraction,
majority voting, fully Connected NN 94% Low Low Breathing from the reflected
signal 26 Accerelemoter

Table 7: Comparison of the non-wearable-based solutions for sleep posture
recognition

#### Summary

Table 6 and Table 7 provide a summary of the work presented for sleep postures
monitoring. Like other categories of sleep monitoring, the wearable-based
posture monitoring solutions are mostly using the accelerometer while the non-
wearable based solutions are using pressure sensor and RF technology.
Different solutions are able to recognize different postures ranging from 4 to
12 postures but almost all solutions presented can recognize the four basic
postures with high accuracy. Validation is one concern for posture recognition
system as there is no benchmark data set available and most solutions either
have to use a video camera to record the ground truth or use another device to
get the ground truth. Video cameras have the privacy concern while other
devices may not give the 100% accurate results. Machine learning plays an
important role in posture recognition as well. Data can be collected through
various sensors but it is the machine learning which can recognize the
postures. Some of the commonly used machine learning algorithms in posture
recognition are KNN, Random Forest, SVM, Naive Bayes (NB), and HMM.

###  4.3 Vital Signs Monitoring

Monitoring vital signs such as respiration rate (RR) and heart rate (HR)
during sleep is very important. These signs provide information about the
overall health of the person and can be used as indicators for the diagnosis
of many disorders. RR can help in the diagnosis of different sleep disorders.
Different people have different HR, depending on person’s health and age. An
abnormal HR may indicate the possibility of some kind of health issues such as
heart problem. According to WHO, around 17 million people die every year
because of cardiovascular disease which counts for 31% of all the deaths
occurring each year. Monitoring HR is very important because it can provide
crucial information about the health of the person and can help in diagnosing
heart problem at early stages. Also, these vital signs can provide information
about different sleep stages as they change in different stages. Owing to the
importance of vital signs during sleep, many works have been conducted in
recent days to monitor the vital signs. In this section, we review some of the
latest works done for vital signs monitoring using the ubiquitous approach.

####  4.3.1 Wearable-Based Approaches for Vital Signs Monitoring

This section presents the wearable-based works for vital signs monitoring
during the sleep. Sharma & Kan [65] proposed an RF-based technique for vital
signs monitoring using a single passive RFID tag attached to the shirt of the
user at the chest area. This work uses the near-field coherent sensing (NCS)
concept for extracting the HR and RR without requiring the skin contact.
Different filters are applied to extract the HR and RR from the phase values
of the reflected signals. Melici et al. [47] presented a magnetometer-based
system which can monitor the breathing rate by measuring the variations in
magnetic field. The magnetometer is packaged in a wearable device along with
an RFduino and is worn around the body using an elastic chest belt. After
applying different filters, a peak detection algorithm is used to calculate
the respiration rate. The performance of the proposed system is compared with
a temperature airflow sensor and the results indicate that the proposed system
can monitor the RR with reasonable accuracy. RF-ECG [76] is another technique
which uses passive RFID tags attached to the shirt (at chest area) of the user
for estimating the heart rate variability. The reflected signal from the
passive tags is cleaned by removing the affect of breathing and the signals
from multiple tags are fused together to strengthen the heartbeat signal.
After applying other filtering techniques such as wavelet-based denoising, a
PCA-based template mechanism is applied to estimate the inter-beat intervals.
Hussain et al. [27] proposed an RFID-based technique for sleep monitoring. Two
passive RFID tags are attached to user’s shirt and the reflected signals from
both the tags are captured by the reader. After removing the ambient noise,
the reflected signals from both tags are fused together to strengthen the
breathing signal. This work uses automatic multiscale-based peak detection
(AMPD) algorithm to estimate the breathing rate. The results of the
experiments show that the proposed technique can achieve good accuracy for
single as well as multiple users. TagBreath [77] is another work which uses
passive RFID tags for breath rate monitoring. TagBreath uses three tags
attached to the user’s shirt for capturing the chest motion resulting from
breathing. Unlike [27] which uses the RSSI values, TagBreath uses the phase
values from the reflected signal and fuses the signals from all three tags.
After applying multiple filters in both frequency and time domain, the breath
rate is estimated by calculating the zero crossing in the selected window.

####  4.3.2 Non-Wearable-Based Approaches for Vital Signs Monitoring

In this section, we present the non-wearable-based studies for vital signs
monitoring during the sleep. SleepSense [41] is a non-contact sleep monitoring
system which can monitor the breathing rate along with other factors such as
bed exit and on-bed movements. This work uses a Doppler radar placed above the
bed which emits a single-tone carrier signal and uses the phase shifts in the
reflected signal as a results of the chest movements due to respiration, to
estimate the breathing rate. The phase shift is proportional to the movement
displacement which can be calculated by demodulating the phase using the an
extended differentiate and cross multiple (DACM) algorithm. Different
statistic, time, and frequency domain feature are extracted and fed into a
decision tree classifier which detect one of the three classes; breathing
section, bed exit, or on-bed movement. An adaptive-threshold based peak
detection algorithm is used to calculate the breathing rate in the breathing
section. Yang et al. [82] proposed a technique for respiration monitoring
using COTS WiFi devices. This work can estimate the breathing rates for
multiple persons simultaneously. The key focus of this work is the optimal
deployment of the WiFi transceivers such that each person is at good location
of only one transceiver and bad location for the other transceiver resulting
in the simultaneous monitoring of multiple persons each by a separate
transceiver.

The work conducted by Sadek & Mohktari [61] uses fiber optic embedded in a
sensor mat to monitor different sleeping factors including RR and HR. The
proposed system uses the mean and standard deviation to identify the sleep
movement and empty bed states. In the sleep state, a Chebyshev band-pass
filter is used to extract RR and HR signals. The HR is calculated using the
Maximal overlap discrete wavelet transform while the RR is calculated using
the peak detection algorithm after smoothing the signal using different
filters. Helena [12] is a recent work which uses geophone (seismic sensor)
integrated in the bed frame to monitor sleep activities including the RR and
HR. Helena first determines the bed status and if the user is on the bed and
there is no movement, the RR and HR are then calculated using an envelop-based
estimation method. After obtaining the envelop, HR can be calculated using the
peak detection algorithm. Since the seismometer is insensitive to lower
frequency measurements (in case of respiration), RR cannot be directly
estimated from the seismic data but instead the detected HR peaks are used to
generate the respiration modulation signal with the help of extrapolation
technique. Then the RR can be estimated by counting the peaks from the
obtained envelop of the modulated signal. The authors have done extensive
experiments to evaluate the performance of Helena. Experiments have been
conducted in lab environments as well as in the wild settings and the results
show that Helena can monitor the vitals signs with high accuracy and in real
time. Turppa et al. [73] uses the frequency modulated continuous wave (FMCW)
radar for vital sign monitoring during sleep. This work uses Fast Fourier
Transform (FFT)-based cepstral analysis method for extracting the HR. For the
RR, an auto-correlation function (ACF) is applied on the phase signal of a
selected range. The proposed system is evaluated in different scenarios
resembling real-world situations including normal and abnormal cases.

#### Summary

A summary of the papers reviewed for vital signs monitoring is given in Table
8 and Table 9. This category of sleep monitoring is dominated by wearable-
based approach. Wearable solutions have high accuracy with low cost. RF
technology is widely used in the wearable solutions in which passive RFID tags
are attached to chest or shirt of the users. Non-wearable solutions are also
using the RF technology in the form of radars and radio devices installed in
the room to capture the sleep data. Unlike the other categories of sleep
monitoring which mostly use the machine learning, signal processing is used
for vital signs estimation. One of the main focus of the presented works is
the use of novel methods for removing the noise from the captured data because
the vital signs can easily be suppressed by the environment noise. Various
filtering techniques have been presented to clean the signal and extract the
RR and HR of the sleeping person. One concern here is the amount of data
collected for the evaluation purposes. Most of the experiments presented in
these works are simulating the sleep and not the actual sleep, i.e., the users
are resting/sitting to simulate the sleep. There is a need of long experiments
which involve the overnight sleep of the user to reflect the real world
scenarios.

Work Vital Signs Sensor-Placement Device Used Methods Used Accuracy
Obtrusiveness Cost Parameter Observed # Subjects Validation Method [65] 2018
HR,RR RFID-chest NA Signal processing 97.58% Low Low RSSI 1 Observation [47]
2018 RR Magnetometer-chest Self-made Signal processing 85% Medium Low Magnetic
vectors 1 Air-flow sensor [76] 2018 HRV RFID tags-shirt ImpinJ tags and reader
Signal processing, DWT, fusion, PCA-based template estimation 93% Low Low
Phase values 15 Heal Force PC-80A ECG Monitor [27] 2019 RR RFID tags-shirt
Alien tags and reader Signal processing 95% Low Low RSSI 4 Observation [77]
2020 RR RFID tags-shirt Impinj reader, Alien tags Signal processing 95% Low
Low Phase values 4 Metronome mobile application

Table 8: Comparison of the wearable-based solutions for vital signs monitoring

Work Vital Signs Sensor-Placement Device Used Methods Used Accuracy
Obtrusiveness Cost Parameter Observed # Subjects Validation Method [41] 2017
RR Doppler Radar- above bed Self-made Decision tree 93.45% Low Low Phase shift
in the reflected signal 3 Nasal airflow sensor [82] 2018 RR WiFi-bedside 5300
NIC, WR841N router Gaussian movement model, Fresnel zone, hampel and wavelet
filters, peak detection 1 bpm(MAE) Low Low Changes caused in received signals
by chest motion 5 Chest strap [61] 2018 RR,HR Optical fiber-sleeping mat Self-
made Signal processing, maximal overlap discrete wavelet transform, peak
detection NA Low Low pressure exerted by body 3 Self-reported survey [12] 2020
RR,HR Seismic sensor-bedframe Self-made Envelope based HR/RR estimation, peak
detection 1.26 bpm,0.52 rpm- 2.41 bpm,NA (MAE),. Low Low Pressure exerted by
body 10+25+2 pulse oximeter( Zorvo), metronome, Apple watch [73] 2020 RR,HR
FMCW radar-above the bed Self-made Signal processing, peak etection, haming
window, autocorrelation function, Hann window 3.6% for HR,9.1% for RR (MAE)
Low Low Changes caused in received signals by chest motion 10 PSG by Embla
Titanium and VTT’s BCG installed under the bedsheet

Table 9: Comparison of the non-wearable-based solutions for vital signs
monitoring

###  4.4 Sleep Disorders

Sleep disorders are different diseases which are attributed to lack of quality
sleep and one of the main reasons of inadequate sleep problem. Sleep disorders
also contribute to other diseases. Some of the most common symptoms of sleep
disorders are irritability, depression or anxiety, tiredness, and daytime
sleepiness [64]. According to the International Classification of Sleep
Disorders (ICSD-3), there are seven types of sleep disorders which are sleep-
related breathing disorders, insomnia, sleep-related movement disorders,
circadian rhythm sleep-wakefulness disorders, central hypersomnolence
disorders, parasomina and other disorders [51]. These disorders can be cured
with proper treatment but the diagnosis of these disorders is a challenging
task. Recently, many in-home techniques have been proposed which can provide
comparable results with clinical methods. In this section, we review some of
the latest works conducted for detection of sleep disorders using the
unobtrusive approach.

####  4.4.1 Wearable-based Approaches for Sleep Disorder Detection

In this section, we review the wearable-based techniques used for sleep
disorders detection. Kye et al. [40] presented a wearable band-based system to
detect periodic limb movements (PLMS). This band consists of inertial sensors
(accerelometer and gyroscope) and is worn on the dorsum of the foot. This work
proposes the motion synchronized windowing (MSW) technique in the feature
extraction phase in which the data stream is segmented into intervals where
the motion occurs. Different classifier are trained and tested but KNN
achieves the highest accuracy for detection of PLMS in the performed
experiments. AutoTag [81] uses the passive RFID tags attached to the chest
area of the user for detection of sleep apnea. This work proposes a novel
technique for mitigating the frequency hopping offset of RFID system. Unlike
the other approaches, AutoTag uses an unsupervised recurrent autoencoder
method for apnea detection and treats the breathing signal as a time sequence
in a time window and uses an LSTM for encoding the breathing signal. The
output of the LSTM is used to estimate the mean and variance vector which in
turn are used for computing the latent vector. Another LSTM is used for
decoding the mean and variance vectors to get the reconstructed breathing
signal. Kullback Leibler (KL) divergence method is used to detect apnea by
comparing the difference between original and reconstructed signals.

Fang et al. [17] proposed an apnea detection system based on the
characteristic moment waveform (CMW) method. This work uses a microphone
attached near the nose of the user to capture the acoustic signals when the
user breaths. In the prepossessing stage, the amplitude contrast of the
original signal is reduced by using different signal processing techniques and
then CMW is used for RR estimation based on time characteristic waveform
(TCW). Jeon & Kang [35] developed a wearable sleepcare kit which can detect
sleep apnea in real-time. This kit consists of two parts: a sensing device
called Bio-Cradle and a control device called personal activity assisting &
reminding (PAAR) band. The Bio-cradle consists of PPG and accelerometer
sensors and a nasal tube for collecting physiological data while the PAAR
works as a scheduler. Sleepcare kit is a plug and play system which doesn’t
require any prior training data and can monitor the sleep in real time using a
mobile application. This system uses the breathing and SpO2 level to detect
apnea and hypopnea. The proposed system can also identify the level of apnea
and can alert the caregivers in real-time using the mobile application.
Bobovych et al. [7] also developed a wearable device which can be worn on
ankle position and can capture two types of leg movements: dorsiflexion (which
are correlated with restless leg syndrome (RLS) and Attention Deficit Hyper
Activity Disorder (ADHD)) and complex leg movements (which are correlated with
sleep fragmentation and brief arousals). Since the proposed system was
supposed to be resource constraint (wearable), a single feature (standard
deviation) is used to train an SVM classifier. The main focus of this work is
not the processing part but rather the power usage analysis of the hardware
and software.

####  4.4.2 Non-Wearable-based Approaches for Sleep Disorder Detection

This section presents the non-wearable-based solutions for sleep disorders
detection. Nandakumar et al. [49] presented a contact-less solution for apnea
detection using the built-in speakers in the smartphones. The speaker in the
phone is used as a sonar by emitting a frequency-modulated sound signal and
the reflection is captured and analyzed to extract the breathing information
of the user sleeping near by. A modified peak detection algorithm is used to
detect different sleeping events such as central apnea, obstructive apnea and
hypopnea based on the breathing pattern. Waltisberg et al. [75] presented a
framework for the detection of sleep apnea and PLMS using the in-bed sensor
system which consists of strain gauges to detect the pressure changes caused
by the breathing motion. This work explores both the measurement fusion and
the decision fusion approaches. PCA and a two-layered feature extraction
approach is used while minimum redundancy maximum relevance criterion (mRMR)
filter is used for feature selection. An NB classifier is used to classify the
sleeping event. The results show that both measurement and decision fusion
approaches give same results and are better as compared to a rule-based
approach using the standard deviation.

EZ-Sleep [25] uses an ambient radio device installed in the bedroom to monitor
insomnia and other sleep activities. The radio device emits RF signals which
are reflected from the human body and the environment. EZ-Sleep analyzes these
reflected signal to extract the sleep-related information such as bed
location, bed-entry, bed-exit, and other insomnia assessment parameters. The
bed is located first using a 2D histogram and image segmentation technique.
Once the bed is located, the next step is to identify whether the user is
lying on the bed or not. EZ-Sleep achieves this by using Hidden Markov Model
(HMM). Before estimating the sleep parameters (vital signs etc.), EZ-Sleep
first needs to detect when the user fall asleep (sleep vs awake
classification). This is done by using CNN with 14-layer residual network
model and to further increase the accuracy for detecting onset sleep, Gradient
Boosting Regressor (GBR) is build on the top of CNN. Results show that EZ-
Sleep can detect different sleep activities with high accuracy. Swangarom et
al. [71] proposed a contact-less system for insomnia detection using the
fabric-pressure sensors embedded in the sleeping mat. The sensors can capture
the body movements resulting from breathing and changing positions. The Athens
insomnia scale (AIS) questionnaire is used for validation of the results. An
SVM with polynomial kernel function is used for classifying the subjects into
two classes; healthy and suffering from insomnia. Sadek et al. [60] proposed
another non-invasive solution for apnea detection using the fiber optic
sensors embedded in a mat and put under the mattress. This work uses the BCG
principle and captures the cardiac movements (HR) and chest movements (RR).
After applying multiple signal processing techniques to clean and strengthen
the signal, an adaptive histogram-based thresholding method is used for apnea
detection.

Work Disorders Sensor-Placement Device Used Methods Used Accuracy
Obtrusiveness Cost Parameter Observed # Subjects Validation Method [40] 2017
PLMS Accelerometer, gyroscope-foot dorsum MetaMotionC KNN 96.92% Low Low Foot
movements 13 Observation [81] 2018 Apnea RFID tags-chest Impinj R420, reader,
Alien tags LSTM, KL divergance 92% Low Low Breathing from phase values of the
reflected signal 4 NEULOG Respiration Belt Sensor [17] 2018 OSA Microphone-
near nose Self-made Signal processing 97.00% Medium Medium Acoustic signal
from breathing 5 Manually [35] 2019 Apnea, hypopnea Accelerometer, PPG-wrist,
nasal airflow Self-made Signal processing, CMW NA Medium Medium SpO2, HR, RR
15 PSG [7] 2020 RLS Capacitive and inertial sensor-ankle Self-made SVM with
linear kernel 90% Low Medium Leg movements 12 PSG, IR camera

Table 10: Comparison of the wearable-based solutions for disorders detection

#### Summary

Table 10 and Table 11 provide a summary of the work presented for sleep
disorders detection. As can be seen from the tables, significant works have
been conducted in sleep disorder detection especially in apnea detection.
Wearable-based solutions have overall better accuracy. Both machine learning
and signal processing are used to detect the disorders. Getting the ground
truth for movement related disorders is one of the concerns in this category.
Again, most of the experiments are simulating the sleep and apnea episodes
rather than involving actual patients who are suffering from these disorders.

Work Disorders Sensor-Placement Device Used Methods Used Accuracy
Obtrusiveness Cost Parameter Observed # Subjects Validation Method [49] 2015
Central apnea, obstructive apnea, and hypopnea Sonar-bedside Four different
smarphones Signal processing, FMCW, peak detection 97% Low Low Chest and
abdomen motions 37 PSG + Video camera [75] 2017 Apnea, hypopnea, PLM Strain
guage sensors-bed Mobility monitor by compliant concept Fusion architecture,
mRMR filter, NB 72% Low High Pressure changes 9 PSG [25] 2017 Insomnia Radio-
room Self-made Signal processing, FMCW, image processing, HMM, CNN NA Low Low
Reflected signal 10 SleepProfiler [71] 2018 Insomnia Pressure sensors-bedsheet
Self-made SVM with polynomial kernel 77.78% Low Low Body pressure 9 AIS
questionnaire [60] 2020 Apnea Fiber optic sensors-bedsheet Self-made Adaptive
histogram-based thresholding 49.96% Low Low Cardiac and chest movements 10 PSG

Table 11: Comparison of the wearable-based solutions for disorders detection

##  5 Open Issues and Future Directions

Although significant work has been conducted in sleep monitoring using the
unobtrusive and in-home solutions, there are still some open issues in this
area which need to be addressed. In this section, we discuss some of those
open issues and provide future research directions.

#### Trade-off between Accuracy and Comfort Level

Accuracy is always an important factor for any solutions. It is more important
in areas like sleep monitoring which consists of vital signs monitoring and
sleep disorder detection. From the related literature we have concluded that
there is a trade off between the accuracy and the comfort level
(unobtrusiveness) of the proposed solutions. Solutions which are more invasive
in nature, provide better accuracy for all the four categories of sleep
monitoring. On the other hand, solutions which are non-invasive and are easy
to use, their accuracy is low in comparison especially in sleep stages
classification and disorder detection. These in-home approaches can be used
for general sleep monitoring but more work is required to bring the accuracy
level to medical grade monitoring. There is a potential for further research
to have solutions which are non-invasive and have the same level of accuracy
as the clinical solutions.

#### Security and Privacy

In sleep monitoring, different sensors (wearable and non-wearable) collect the
physiological data of the sleeping person. This data is then sent to either an
edge device or to a central module (could be cloud) for further processing
(recognition). In some cases, the processing entity is installed along with
the sensors (in form of microcomputers, aurdinos, etc.) and the results are
transferred via the network. From the related literature, we can see that the
focus is mainly on the accuracy and ease of use. Very little (and mostly none)
efforts are made for the security and privacy of the collected data. Most of
the data is medical data and should not be shared with unauthorized person but
no specific measures are taken to protect the data from unauthorized access.
Since the areas like telemedicine and remote monitoring are gaining popularity
in the healthcare, there is a need for adapting specific measure to protect
the data. Hathaliya & Sudeep [22] studied the security and privacy issues in
health care and identified some of the key challenges.

#### Limited Data

Data is a main aspect of sleep monitoring. Publicly available data sets are
very important for evaluating and comparing the performance of any newly
proposed solution. From the literature, we found that there are some data sets
publicly available but most of them are of PSG data collected in a clinical
environment. These data sets can help in the performance evaluation of
solutions which use the same technique i.e., PSG or ECG etc., but nowadays
many solutions are unobtrusive and use wearable and now-wearable devices for
monitoring physiological signals other than PSG. These solutions are
implemented in home environments and use devices such as radio, RFID,
accelerometer, magnetometer, pressure sensors, and WiFi. There is no data sets
publicly available for these type of approaches except [56] and [24]. None of
the related works covered in Section 4 except [89] have released their data
set. Since the use of intrusive technique is increasing for sleep monitoring,
there is a need for benchmark data sets to evaluate the performance of new
solutions. The problem of limited data can be solved by user contributions
i.e., crowdsourcing the sleep data from their in-home devices. Also, the
authors can be encouraged to share their data sets for validation purpose.
Hence, large data sets can be created which are more balanced and diverse to
reflect the real-world scenarios.

#### Need for Sleep-Specific Devices

From the related literature, it can be seen that many devices which are used
for sleep monitoring, are the commercially available general purpose devices
mainly used for activity tracking. Sleep monitoring is often considered as an
additional feature of these devices. In some cases, the researchers have
customized these devices to use it for sleep monitoring. Acceptance by the
users is one of the main aspect of wearable devices. A study conducted by Shin
et al. [66] reported that it is unreasonable to design an all-purpose wearable
activity tracker. Although some sleep-specific devices exist in the market,
there is a need of more research and development to produce specific sleep-
monitoring devices that are cheap and easy to use for both short-term and
long-term monitoring.

#### Use of Machine Learning

After collecting the physiological signals, two approaches can be used to
infer the sleep related information. These approaches are signal processing
and machine learning. We have found in the literature review, that the use of
machine learning is increasing for sleep monitoring. Many studies have used
machine learning and have explored the feature engineering to get good
results. One of the advantages of using machine learning is the high accuracy
for sleep monitoring especially for detecting disorders, sleep stages, and
postures. However, machine learning requires more resources which is a problem
for wearable devices given their small sizes and battery requirements. Some
solutions have tried to tackle this problem by shifting the training phase
offline [33] while other have used light weight techniques to ease the burden
on the wearable devices.

#### Long-Term vs Short-Term Monitoring

The related literature shows that unobtrusive approaches (both wearable and
non-wearable) are evaluated only for the short-term monitoring (mostly a
single night or a few hours). However, it would be interesting to see the
performance for a long-term monitoring. Despite the low accuracy of
unobtrusive approaches as compared to clinical solutions, an advantage of the
unobtrusive solutions is their ease of use and low cost for long-term
deployment. These solutions can be explored for long-term in-home monitoring
and can help in the early diagnosis of many health relate problems such as
epilepsy [44], depression [16] and other respiratory problems [4].

##  6 Conclusion

Sleep monitoring has been an important approach to understand sleep behaviors
and improve sleep quality. With the recent advancements in sensor
technologies, unobtrusive sleep monitoring using wearable or non-wearable
sensors has become an active research area. In this paper, we have presented a
comprehensive review of the latest research efforts in the area of sleep
monitoring. We divide the related literature into four categories: sleep
stages classification, sleep postures recognition, sleep disorders detection,
and vital signs monitoring. Unlike the previous surveys which focus on some of
the categories in sleep monitoring, we cover all the four categories. The main
focus of this survey is the unobtrusive techniques used for in-home sleep
monitoring. We review the latest work in all the four categories of sleep
monitoring and provide a comparison based on 10 key factors. We also identify
and discuss some open research issues and provide future research directions
to stimulate further research in this important topic.

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