# IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for
Better Well-Being

Amelie Gyrarda,c,*, Seyedali Mohammadib, Manas Gaurb and Antonio Kunga CONTACT
A. N. Author. Email<EMAIL_ADDRESS>aTrialog, Paris, France;
bUniversity of Maryland, Baltimore County (UMBC), USA;
cMachine-to-Machine Measurement (M3), Paris, France

###### Abstract

Sustainable Development Goals (SDGs) give the UN a road map for development
with Agenda 2030 as a target. SDG3 "Good Health and Well-Being" ensures
healthy lives and promotes well-being for all ages. Digital technologies can
support SDG3. Burnout and even depression could be reduced by encouraging
better preventive health. Due to the lack of patient knowledge and focus to
take care of their health, it is necessary to help patients before it is too
late. New trends such as positive psychology and mindfulness are highly
encouraged in the USA. Digital Twin (DT) can help with the continuous
monitoring of emotion using physiological signals (e.g., collected via
wearables). Digital twins facilitate monitoring and provide constant health
insight to improve quality of life and well-being with better personalization.
Healthcare DT challenges are standardizing data formats, communication
protocols, and data exchange mechanisms. To achieve those data integration and
knowledge challenges, we designed the Mental Health Knowledge Graph (ontology
and dataset) to boost mental health. The Knowledge Graph (KG) acquires
knowledge from ontology-based mental health projects classified within the
LOV4IoT ontology catalog (Emotion, Depression, and Mental Health).
Furthermore, the KG is mapped to standards (e.g., ontologies) when possible.
Standards from ETSI SmartM2M, ITU/WHO, ISO, W3C, NIST, and IEEE are relevant
to mental health.

###### keywords:

Well-Being; Mental Health; Standard; Semantic Web Technologies; Health
Ontology; Knowledge Graph; IoT Ontology Catalog; Large Language Model.

††articletype: ARTICLE TEMPLATE

> "An ounce of prevention is worth a pound of cure."
>
> Benjamin Franklin

> "How to gain, how to keep, how to recover happiness is in fact for most men
> at all times the secret motive of all they do, and of all they are willing
> to endure."
>
> William James

##  1 Introduction

More than one-fifth of adults in the United States have dealt with mental
health issues, according to the National Institute of Mental
Health111https://www.nimh.nih.gov/health/statistics/mental-illness. This
situation has led to the government setting aside $280 billion to improve the
availability and quality of mental health
services222https://www.whitehouse.gov/cea/written-
materials/2022/05/31/reducing-the-economic-burden-of-unmet-mental-health-
needs/. Mental health can increase productivity and efficiency, improve staff
morale, and reduce absenteeism (Albraikan, 2019). There are numerous reviews
on mental health using Wearable sensors and Artificial Intelligence Techniques
(Gedam Paul, 2021). Sustainable Development Goals
(SDGs)333https://sdgs.un.org/goals give the UN a road map for development with
Agenda 2030 as a target. SDG3 "Good Health and Well-Being" ensures healthy
lives and promotes well-being for all ages. Digital technologies can support
SDG3. We review hereafter definitions relevant to mental health, the need for
Digital Twin for Mental Health, and the benefit of a Knowledge Graph (KG).
Mental health444https://www.who.int/health-topics/mental-health#tab=tab_1 is a
state of mental well-being that enables people to cope with the stresses of
life, realize their abilities, learn well, and work well, and contribute to
their community. It has intrinsic and instrumental value and is integral to
our well-being. IEEE 7010 defines well-being as "the continuous and
sustainable physical, mental, and social flourishing of individuals,
communities, and populations where their economic needs are cared for within a
thriving ecological environment." Stress vs. Anxiety: People under
stress555https://www.apa.org/topics/stress/anxiety-difference experience
mental and physical symptoms, such as irritability, anger, fatigue, muscle
pain, digestive troubles, and difficulty sleeping. Anxiety, on the other hand,
is defined by persistent, excessive worries that don’t go away even in the
absence of a stressor666https://www.apa.org/topics/stress/anxiety-difference.
Burnout and even depression777https://www.who.int/news-room/fact-
sheets/detail/depression could be reduced by encouraging better preventive
health. Lack of patient knowledge and focus to take care of their health
before it is too late. New trends such as positive psychology (Seligman, 2008)
and mindfulness (MBSR) (Kabat-Zinn, 2003) are highly encouraged in the USA.
Depression is considered the main mental health crisis by the World Health
Organization (WHO) (Mullick ., 2022). Mental health includes emotional,
psychological, and social well-being changes. DSM-V (Diagnostic and
Statistical Manual of Mental
Disorders)888https://www.psychiatry.org/psychiatrists/practice/dsm references
more than 70 mental disorders that complement the International Classification
of Diseases (ICD). DSM-V helps clinicians and researchers define and classify
mental disorders, which can improve diagnoses, treatment, and research. DSM-V
provides a form with checklists of symptoms for better diagnosis. Burnout does
NOT appear in DSM and ICD. Mental illness is a type of health condition that
changes a person’s mind, emotions, or behavior and has been shown to impact an
individual’s physical health (Su, 2020). Depressive disorders, or unipolar
depression’s symptoms are low mood, loss of interest in day-to-day activities,
significant weight changes, reduction of mobility, constant fatigue,
difficulty concentrating, and feelings of worthlessness that can be diagnosed,
and severity, using the Patient Health Questionnaire-9 (PHQ-9) (Gutierrez .,
2021).

The necessity of AI-enabled IoT Digital Twin using knowledge graph for
achieving SDG-3: An IoT Digital Twin designed for proactive mental health care
corresponds with four of the United Nations’ Sustainable Development Goals.
First is good health and well-being (SDG-3), which aims to ensure healthy
lives and promote well-being for all age groups. We provide a pragmatic
approach to developing IoT Digital Twin using domain-specific standards and
knowledge graphs, which would advance mental health care and well-being.
Disparities in mental health care access are prevalent, varying among regions,
socioeconomic statuses, and genders. By prioritizing preventive strategies
using domain-specific standards (e.g., questionnaires, guidelines) and
leveraging AI-enabled IoT technology, this endeavor can potentially mitigate
these access gaps, aligning with initiatives like the NIH’s AIM-AHEAD
Initiative.

Why do we need a Digital Twin for Mental Health? The StandICT landscape of
Digital Twins (DT)999https://www.standict.eu/landscape-analysis-
report/landscape-digital-twin reminds that "Digital Twin" was first introduced
by Professor Michael Grieves from the University of Michigan in 2002.
According to ISO/IEC 30173 Digital twin – concepts and
terminology101010https://www.iso.org/standard/81442.html, DTs are defined as a
"digital representation of a target entity with data connections that enable
convergence between the physical and digital states at an appropriate rate of
synchronization." Notable implementation of DTs include Siemens Healthineers
(Erol ., 2020), IBM Maximo Application
Suite111111https://www.ibm.com/products/maximo and Philips
HeartModel121212https://www.philips.com/a-w/about/news/archive/blogs/innovation-
matters/20181112-how-a-virtual-heart-could-save-your-real-one.html. DT can
help with emotion monitoring using physiological signals (e.g., collected via
wearables). Healthcare DTs facilitate monitoring, understanding, and
optimization of human functioning and monitor health to improve quality of
life and well-being (Laamarti ., 2020), (Ferdousi ., 2022), (Albraikan, 2019),
(Anand Dhanalakshmi, 2024) with better personalization (Bagaria ., 2020) by
using digital technologies such as IoT, AI, etc. Healthcare DT challenges
highlight the need for standardizing data format, communication protocols, and
data-exchange mechanisms (Turab, 2023).

Why do we need Knowledge Graphs for Mental Health? The European Human Brain
Project (150 000 000 Euros, 161 partners), more precisely, the EBRAINS
KG131313https://search.kg.ebrains.eu/?facet_type[0]=Dataset&q=cortisol, is a
synergy project between neuroscience, computing, informatics, and brain-
inspired technologies, encourages open-science through a web-based system to
share tools, etc. However, when looking for keywords such as cortisol (the
stress hormone), nothing can be found. KGs (Sheth ., 2019) can use ontologies
to structure data. An ontology provides a shared common understanding of a
domain (Gruber, 1995). The use of ontologies is already recognized in the
biomedical domain with BioPortal ontology
catalog141414https://bioportal.bioontology.org/. Ontology Development 101
methodology (designed by the creators of the Standford Protégé Ontology Editor
tool) encourages reusing domain knowledge by reusing ontologies in Step 2
"Consider reusing ontologies."

In this paper, we describe about semantic interoperability applied to mental
health, which is a follow-up of our health IoT semantic interoperability past
work (Gyrard Sheth, 2020) (Gyrard ., 2022). Toward Converging Standards and
Semantic Web Technologies applied to Mental Health: We illustrate the vision
of our aligning technologies on Semantic Web and Data spaces and Standards
such as ISO, IEC, and W3C as depicted in Figure 1. W3C Standards are more
adapted to Semantic Web technologies such as W3C RDF, W3C RDFS, W3C OWL, W3C
SPARQL, etc. Semantic Web technologies include knowledge Graphs with
ontologies and Linked Data. There are also ISO standards about ontologies such
as ISO/IEC 21838-1:2021 Information technology — Top-level ontologies (TLO) —
Part 1: Requirements, ISO/IEC 21838-2:2021 Information technology — Top-level
ontologies (TLO) — Part 2: Basic Formal Ontology (BFO), etc. Data Space
initiatives such as BDVA, Gaia-X, etc. are considered. Some energy data spaces
are designed by projects such as EDHS2, and TEHDAS. The list is not
exhaustive. "Charting past, present, and future research in the semantic web
and interoperability" highlights the importance of semantic interoperability
(Rejeb ., 2022) and on our past work: Table 5 "Top 20 most productive authors"
and Figure 4 "Co-authorship network." The recommendation is that
interoperability should remain a primary research focus and closer
collaborations with industry, governments, and standards organizations to
develop more effective models.

Figure 1: Standards, Semantic Web and Data Spaces applied to Health

Our vision has been shared with the EUCloudEdgeIoT Concentration and
Consultation Meeting on Computing Continuum: Uniting the European ICT
community for a digital future in May
2023151515https://eucloudedgeiot.eu/concentration-and-consultation-meeting-on-
computing-continuum-uniting-the-european-ict-community-for-a-digital-future/.

The remainder of this chapter is organized as follows: related work
limitations in Section 2, standards for mental health in Section 3, the mental
health ontology catalog in Section 4 (which includes mapping to standards
ontologies), the project use cases in Section 5, and conclusion in Section 6.

##  2 Related Work: IoT, Digital Twin, and AI for Mental Health, Stress and
Well-Being

This section reviews IoT-based Mental Health Monitoring Applications, IoT-
based Stress Detection, Digital Twin for Mental Health, and AI-based Mental
Health.

Authors | Year | Research Problem | Sensor or | Reasoning
---|---|---|---|---
|  | Addressed & Project | Measurement Type |
Garcia-Ceja . | 2018 | Mental Health Monitoring |  ✓Heart rate, GSR, | Survey paper
|  | Systems (NHMS) Survey | body or skin temperature |  ✓ML algorithm
Kim | 2017 |  Depression Severity |  ✓Infrared motion sensor |  ✓Bayesian Network
|  | Elderly People, AAL |  | Decision Tree, SVM, ANN
Zhou . | 2015 | Monitoring mental health |  ✓Heart rate, pupil variation, |  ✓ML (Logistic
|  | states | head movement, eye blink, facial expression | regression, SVM)
Garcia-Ceja . | 2016 | Stress |  ✓Accelerometer data (from smartphone) |  ✓Naive Bayes, Decision Tree
Yoon . | 2016 | New stress monitoring patch |  ✓Skin conductance, pulse wave, skin temperature |  ×-
Lu . | 2012 |  StressSense |  ✓Voice data (smartphone) |  ✓GMMs
Kh. Chang . | 2011 | AMMON: Stress detector |  ✓Voice data |  ✓SVM
Erol . | 2020 |  DT for patients and medical devices |  ×No |  ×No
Liu . | 2019 | CloudDTH - DT healthcare |  ✓Yes Wearable medical devices |  ×No
Albraikan | 2019 | inHarmony - Emotional Well-being DT |  ✓Yes Empatica E4 |  ×No

Table 1: Digital Twin for Mental Health, Depression, Stress, and Well-Being:
sensors, reasoning, and applications.

IoT-based Mental health and depression monitoring applications are summarized
in Table 1. 23 Mental Health Monitoring Systems (NHMS) Garcia-Ceja . (2018)
using sensor data provided by IoT devices and analyzed with Machine Learning
are classified according to the study type (e.g., bipolar disorder detection,
migraine forecasting, depression detection, anxiety detection, stress
detection, social phobia association, bipolar disorder association, anxiety
association, depression association, and epilepsy). Various devices are
employed (e.g., eye sensor, heart rate variability, electrodermal activity,
wrist accelerometer, galvanic skin response, skin conductance, spo2,
photoplethysmogram, accelerometer, gyroscope, pressure sensitive, video
cameras, VR headset, pupil-corneal reflection and head tracker, SMS, calls,
screen, GPS, location, touchscreens, audio, contacts, videos, sound, head-
mounted display, questionnaires). Conclusion: We focus on IoT devices relevant
to mental health disorders such as stress, anxiety, and depression, and do not
consider others such as bipolar, migraine, or phobia. The authors do not
mention at all the term "ontology" or knowledge graph."

24 IoT-based mental health care applications Gutierrez . (2021) (bipolar
disorders, depression, schizophrenia, and stress-related disorders); from 2010
to 2020 are designed for: 1) data acquisition, 2) self-organization, 3)
service level agreement, and 4) identity management during mental health
interventions. IoT devices considered are proximity sensors, ambient light,
accelerometers, gyroscopes, magnetometers, ambient sound, barometers,
temperature, and humidity. Diagnostic and Statistical Manual of Mental
Disorders 5 (DSM 5) categorizes the mental health disorder literature.
HealthyOffice smartphone app Zenonos . (2016) focuses on eight mood state
recognition (Excited, Happy, Calm, Tired, Bored, Sad, Stressed, Angr) in work
environments.

Conclusion: We focus on mental health disorders such as stress, and
depression, and do not consider other disorders such as bipolar, or
schizophrenia. Gutierrez et al. Gutierrez . (2021) only cite the mental health
ontology from Hazdic et al. Hadzic . (2008), and do not mention at all the KG
term, so key research on depression ontologies and KGs are missing. For this
reason, we built the ontology catalog as introduced in Section 4.1 and Table
2.

IoT-based Stress Detection is summarized in Table 1. Stress is a reaction from
the human body in response to a challenging event or a demanding condition
Gedam Paul (2021); which can be detected using wearable sensors (e.g.,
Electrocardiogram, Electroencephalography, and Photoplethysmography) and
applied machine learning. Activities such as driving, studying, and working
are taken into consideration. Wearable healthcare-monitoring systems for pain
and stress detection J. Chen . (2021)) are using wearable sensors to measure
physiological signals such as heart, brain, muscle, electrodermal,
respiratory, blood volume pulse, and skin temperature. A system to detect,
diagnose and manage mental health emergencies Mullick . (2022); is based on
four physiological parameters: heart rate, Spo2, body temperature (LM35
sensor), pressure (BMP180 sensor).

Digital Twin (DT) for Mental Health and Well-Being is summarized in Table 1.
Digital Twin Coaching for Physical Activities’s survey (Gámez Díaz ., 2020)
explore papers from Scopus, Web of Science, IEEE Xplore and ACM Digital
Library the last ten years (2010–2020). The papers have been classified into:
1) sports, 2) wellbeing, and 3) rehabilitation. The following information is
extracted: 1) ML algorithms being used 2) Type of application being
researched, 3) Sensors and actuators devices used, 4) Performance of the ML
algorithm, 5) Usability feedback of users about the system. Digital Twin in
healthcare (Turab, 2023) highlights the need for standardizing data format,
communication protocols, and data-exchange mechanisms. Turab et al. highlight
that ISO has an interest in Digital Twin. Indeed, we are involved in ISO SC 41
IoT and Digital Twin. Well-being digital twin (WDT) (Ferdousi ., 2022)
challenges for predictive well-being applications are technical issues of
handling heterogeneous data and standards, data bias, level of autonomy, trust
in intelligence, data visualization issues, and consent of humans. Drawbacks
of WDT are: 1) Inadequate or missing data, 2) Ethical overheads, 3) Trust in
AI, and 4) Necessity of domain knowledge. Well-being digital twin (WDT) areas
are: 1) Collecting and managing vast healthcare data, 2) Meaningful data
visualization, 3) Facilitating predictive healthcare, 4) Improving healthcare
quality of experience (QoE), 5) Personalized healthcare system (e.g., digital
coaching, elderly healthcare (e.g. device in Ponmalar Anand (2024)), immune
system care), and 6) Understanding clinical pathways. An ISO/IEEE 11073
standardized digital twin framework architecture for health and well-being to
analyze data from personal health devices is compliant or not with X73
standards (Laamarti ., 2020). inHarmony (Albraikan, 2019) is an Emotional
Well-being DT for workplace which includes emotion detection, emotional
biofeedback, and emotion-aware recommender systems. A usability study was
carried out with 35 practitioners wearing the Empatica E4 sensor to acquire
physiological signals. CloudDTH (Liu ., 2019) is a DT Healthcare (DHT)
framework for real-time monitoring, diagnosing, and predicting of elderly’s
health, based on cloud, and wearable medical devices technologies. A Big Data-
based Precision Medicine Cloud Platform is designed to display human
physiological health data for data management, data analysis, etc. DTs of
patients and DTs of medical devices are considered (Erol ., 2020). DT Siemens
Healthineers (Erol ., 2020) for radiology department in Mater Private
Hospitals from Ireland to reduce patient’s waiting time. Siemens Healthineers
is also applied to cardiologists in a research project at Heidelberg
University for patients with chronic congestive heart failure. Philips
HeartModel161616https://www.philips.com/a-w/about/news/archive/blogs/innovation-
matters/20181112-how-a-virtual-heart-could-save-your-real-one.html is a
personalized Digital Twin of the heart to help surgeons with real-time 3D. Its
HeartNavigator tool combines the Computed Tomography (CT) heart anatomy
obtained before the surgical procedure and live during the surgery. Sooma is a
startup that simulates the electrical signals of the brain to treat
depression, other neurological and psychiatric disorders.

Conclusion: We did not find a digital twin for mental health. inHarmony is
addressing well-being.

AI-based Mental Health: "Positive AI" (van der Maden ., 2023), means that AI
systems actively and intentionally support human well-being based on fields
such as positive psychology, human-centered design, and computing. The authors
categorized twelve well-being AI challenges into 1) knowledge (how to
conceptualize, operationalize, optimize for, and design), and 2) motivation
(misaligned incentives, PR & monetary risks, and lack of access, preventing).
To identify mental health states, machine learning algorithms such as support
vector machines, decision trees, naïve Bayes classifier, K-nearest neighbor
classifier, and logistic regression are used (Srividya ., 2018).

Related work shortcomings:  Those works do not use health standards that we
are addressing in Section 3. Furthermore, in this chapter, we will focus on
KGs (e.g., ontologies) as AI solutions as explained in Section 4.

##  3 Standards for Mental Health and Well-Being: Artificial Intelligence
(Semantic Web, Ontologies)

Figure 2: Standardized Ontologies

We investigate Standards Development Organizations (SDOs) for (mental) health
covering AI (Semantic Web, Ontologies) such as: ETSI SmartM2M EHealth/Ageing-
Well Ontology in Section 3.1, ITU/WHO Focus Group on Artificial Intelligence
for Health in Section 3.2, ISO Health Standards in Section 3.3, W3C Health
Standards in Section 3.4, NIST Health Standards in Section 3.5, and IEEE
Health Standards in Section 3.6. Standards Development Organizations SDOs
define health ontologies or IoT ontologies describing sensors to measure the
physiological signals of patients as depicted in Figure 2.

###  3.1 ETSI SmartM2M SAREF for EHealth/Ageing-Well Ontology

ETSI Smart M2M SAREF4EHAW ontology (ETSI TS 103 410-8 V1.1.1 (2020-07)
SmartM2M; Extension to SAREF; Part 8: eHealth/Ageing-well Domain, 2020)
reviews standards such as IEEE, ETSI, SNOMED, OneM2M), Alliances (AIOTI), IoT
Platforms, and European projects and initiatives, etc.

The use cases are classified into some of those categories: 1) Daily Activity
Monitoring, 2) Cognitive simulation for mental decline prevention, 3)
Prevention of social isolation, 4) Comfort and safety at home. SAREF4EHAW
investigated the following ontologies: 1) WSNs/measurement ontologies: OGC
(Open Geospatial Consortium) Observations & Measurements (O&M), Sensor Model
Language (SensorML), Semantic Sensor Web (SWE): W3C & OGC SOSA (Sensing,
Observation, Sampling, and Actuation) and W3C SSN (Semantic Sensor Network).
NASA QUDT (Quantities, Units, Dimensions, and Types). 2) eHealth/Ageing-well
domain main ontologies: ISO/IEEE 11073 Personal Health Device (PHD) standards,
ETSI SmartBAN Reference Data Model and associated modular ontologies, FHIR RDF
(Resource Description Framework), FIESTA-IoT Ontology to support the
federation of testbeds, Bluetooth® LE (Low Energy) profiles for medical
devices proposed by Zontinua, MIMU-Wear (Magnetic and Inertial Measurement
Units) ontology, and Active and Healthy Ageing (AHA) platform wearables’
device ontology. The document explains that SAREF has been mapped with oneM2M
base ontology in 2017. SAREF4EHAW (ETSI TS 103 410-8 V1.1.1 (2020-07)
SmartM2M; Extension to SAREF; Part 8: eHealth/Ageing-well Domain, 2020)
selected 43 ontological requirements, and 59 service-level assumptions of the
eHealth/Ageing-well domain (use cases included). For instance, as a
requirement, the ontology describes ECG ((Muthalagu ., 2023)) device concepts.
There is also SAREF4WEAR 171717https://saref.etsi.org/saref4wear/ an extension
for wearables.

Conclusion: SAREF4EHAW demonstrates the need for ontologies. We did not find
standards more specific to mental health in ETSI.

###  3.2 ITU/WHO Focus Group on Artificial Intelligence for Health

ITU/WHO Focus Group on Artificial Intelligence for Health (FG-
AI4H)181818https://www.itu.int/go/fgai4h, established in 2018, free and open,
partners with the World Health Organization (WHO) to standardize AI-based
assessment framework and evaluation for health, diagnosis, triage or treatment
decisions. It provides an AI for health online platform and complementary
tools for benchmarking of data. FG-AI4H comprises experts: machine learning/AI
researchers, healthcare practitioners and researchers, regulators,
representatives of health ministries and ministries of telecommunication,
international organizations, and individuals from complementary fields. The FG
highlights the need for explainability and interpretability of AI tools.
ITU-T/WHO FG-AI4H use cases: 24 AI4H use cases are introduced, including
traditional medicine, psychiatry, neurological disorders, falls among the
elderly, etc. DEL.10.23 TG-AI for Traditional Medicine reviews the existing AI
solutions for Traditional Medicine. AI in traditional medicine will be
relevant to support healthcare practitioners in recommending integrative
medical practices. Traditional Medicine treatments: 1) Ontological and Natural
language processing (NLP) are encouraged to extract useful information, 2)
Analyzing and integrating data, and 3) Decision support, 4) Predictive
analytics, 5) Patient monitoring and feedback, 6) virtual health assistants,
and 7) Research and knowledge discovery.

Conclusion: We find a standard on AI Traditional Medicine, but we did not find
standards for mental health in this SDO.

###  3.3 ISO Health Standards

The CEN/ISO EN13606 is a European norm approved as an ISO standard to achieve
EHR semantic interoperability. ISO 13606-5:2010 Health Informatics -
Electronic Health Record communication
standards191919http://www.en13606.org/information.html defines an architecture
for exchanging Electronic Health Record (EHR) describing patient’s health
status and ease communication between EHR systems (e.g., clinicians
applications, decision support systems). ISO 13606 seems an open standard.
Within the ISO SC41 IoT and Digital Twin, there is an ongoing standard
entitled "IoT/IEC 30197 IoT for Stress Management, Good health & Well-being".
ISO SC42 AI - "AI-enabled Health Informatics" is a joint working group that
will provide a landscape survey and a set of recommendations for future work
on the impact of ISO/TC 215 standards. The group comprises experts from
ISO/IEC/JTC 1/SC 42, IEEE, and the ITU/WHO AI4Health focus group. ISO/IEEE
11073 medical device communication standard - Used by Personal Connected
Health Alliance (PCHAlliance) - X73 standards facilitates health data exchange
while providing plug-and-play real-time interoperability by being compliant
with the X73 communication model. However, the X73 standards do not address
security or users’ privacy. This ISO/IEEE 11073 standard is used by Laamarti .
(2020) for a digital twin framework architecture for health and well-being in
smart cities. NIST researchers are collaborating with medical device experts
to develop standards for medical device communications to enhance semantic
interoperability. XML schema and tool based on the ISO/IEEE 11073 medical
device communication standard is developed by Garguilo et al. (Garguilo .,
2007). Personal Connected Health Alliance (PCHAlliance) products use the
ISO/IEEE 11073 Personal Health Data (PHD) Standards.

Conclusion: ISO 215 Health Informatics must be explored further to dig any
documents related to mental health. CEN/ISO EN13606 focuses on EHR semantic
interoperability. ISO SC42 AI joint working group “AI-enabled Health
Informatics” comprises experts from ISO/IEC/JTC 1/SC 42, IEEE, and the ITU/WHO
AI4Health focus group.

###  3.4 W3C Semantic Web Health Care and Life Sciences Community Group (HCLS
CG)

W3C Semantic Web (SW) Health Care and Life Sciences Community Group (HCLS
CG)202020https://www.w3.org/community/hclscg/ encourages the use of SW
technologies across health care, life sciences, clinical research and
translational medicine as they need interoperability of information from many
disciplines. HCLS CG use SW technologies to design use cases which have a
clinical, research of business values. The CG develops liaisons with
organizations in healthcare, life sciences, and clinical research, including
organizations that are working on standards.

Conclusion: W3C HCLS CG highlights the need of using Semantic Web. The final
report is more focused on drug-drug interaction. We did not find standards
specific to mental health.

###  3.5 NIST Health Standards

NIST researchers are collaborating with medical device experts to develop
standards for medical device communications to enhance semantic
interoperability. XML schema and tool based on the ISO/IEEE 11073 medical
device communication standard (Garguilo ., 2007).

Conclusion: NIST has an interest in semantic interoperability.

###  3.6 IEEE Health Standards

IEEE digital health standards can help save lives and improve people’s quality
of life. Standards aim to share information across end-to-end infrastructure,
particularly: 1) interoperability: information exchange among organizations,
2) cost savings/efficiency (e.g., for hospitals), etc. We selected four
standards: 1) IEEE 7010’s definition of well-being (mentioned earlier), 2)
IEEE 1752.1-2021 Standard for Open Mobile Health Data–Representation of
Metadata, Sleep, and Physical Activity Measures, 3) ISO/IEEE 11073 medical
device communication standard, and 4) IEEE P1157 Medical Data Interchange
(MEDIX). IEEE 1752.1-2021 Standard for Open Mobile Health Data–Representation
of Metadata, Sleep, and Physical Activity
Measures212121https://standards.ieee.org/ieee/1752.1/6982/ eases semantic
interoperability across mobile health sources and provides meaningful
description, exchange, sharing, and use of such mHealth data for consumer
health, biomedical research, and clinical care stakeholders. NIST researchers
are collaborating with medical device experts to develop standards for medical
device communications to enhance semantic interoperability. XML schema and
tool based on the ISO/IEEE 11073 Medical Device Communication standard
(Garguilo ., 2007). IEEE P1157 Medical Data Interchange (MEDIX) is a standard
for communication of medical information between heterogeneous healthcare
information systems such as between a patient care system and selected
ancillaries in the medical center setting. IEEE 1157 Standard for Health Data
Interchange is designed with health care professionals.

Conclusion: Within IEEE 7010, we found a standard explaining the definition of
well-being. Understanding well-being will help as a preventive solution to
reduce mental health problems.

###  3.7 Standards: Conclusion

We did not find standards for mental health in those SDOs: ETSI SmartM2M,
ITU/WHO FG-AI4H, and W3C HCLS CG. ISO 215 Health Informatics must be explored
further to dig any documents related to mental health. CEN/ISO EN13606 focuses
on EHR semantic interoperability. We are involved in "IoT for Stress
Management, Good health & Well-being, a standard under development within ISO
SC41 IoT. ISO SC42 AI joint working group “AI-enabled Health Informatics”
comprises experts from ISO/IEC/JTC 1/SC 42, IEEE, and the ITU/WHO AI4Health
focus group. NIST has an interest in semantic interoperability. IEEE 7010
focused on well-being which encourages us to investigate well-being as a
preventive solution to reduce mental health problems.

##  4 LOV4IoT Ontology Catalog for IoT-Based Depression and Mental Health KGs

Ontology catalogs such as BioPortal (Noy ., 2009), Linked Open Vocabularies
(LOV) (Vandenbussche ., 2016) do not cover sensors (Internet of Things). For
this reason, we built the LOV4IoT ontology catalog (introduced in Section
4.1), with a subset specific to mental health and depression (demonstrated in
Table 2), and emotion ontologies explained in (Gyrard ., 2021) (Gyrard
Boudaoud, 2022) or LOV4IoT-Health to collect sensor used, etc. Mapping to
Standards such as ETSI SmartM2M SAREF4EHAW is mentioned in Section 4.2.
Mapping to Standardized Health KGs/Ontologies/Terminologies such as SNOMED-CT,
FMA, RXNORM, MedDRA, LOINC, ChEBI, or well-known knowledge graphs such as
DBpedia is described in Section 4.3.

###  4.1 LOV4IoTMental Health Ontology Catalog and Knowledge Graph

We have designed an ontology catalog for depression and mental health, called
LOV4IoT Mental Health 222222http://lov4iot.appspot.com/?p=lov4iot-depression
(Table 2).

Authors | Year | Project | Ontology-based | Reasoning
---|---|---|---|---
|  |  | project |
Hastings et al. Hastings . (2012) | 2012 | Mental Disease (MD) | ✓(online code) | No
Ceusters et al. Smith et al. |  | Ontology |  |
Amoretti et al. Amoretti . (2019) | 2019 | Mental Disorder | ✓(online code) | OWL-DL
|  | Schizophrenia ontology |  |
Chang et al. YS. Chang . (2013) | 2015 |  Depression ontology | ✓(but not shared) | Bayesian networks
Chang et al. YS. Chang . (2015), Taiwan | 2013 |  |  | Jena rule (Dysthymia), 46 inference rules
Huang et al. Huang . (2017) | 2017 | DepressionKG | ✓(datasets) | No
Hadzic et al Hadzic . (2008) | 2008 |  Mental Health Ontology | ✓(but not shared) | No
Perth, Australia |  | disorder, factors, and treatments |  |
Jung et al. Jung . (2017) Jung . (2015), Korea | 2017-2015 |  Depression Ontology, adolescent population, Twitter analysis | ✓(but not shared) | No

Table 2: Ontology-based depression and mental health projects

GENA (Graph of mEntal-health and Nutrition Association) (Dang .,
2023)232323https://github.com/ddlinh/gena-db encodes relationships between
nutrition and mental health. GENA describes food, biochemicals, and mental
illnesses extracting knowledge from PubMed. GENA consists of 43,367
relationships with concepts such as nutrition, biochemical, mental health,
chemical, and disease. GENA used ontologies such as Human Disease Ontology
(DOID), Chemical Entities of Biological Interest Ontology (CHEBI),
Foundational Model of Anatomy (FMA), Disorders cluster (APADISORDERS), Autism
Spectrum Disorder Phenotype Ontology (ASDTTO), The FoodOn Food Ontology
(FOODON), MFO Mental Disease Ontology (MFOMD), Protein Ontology (PR), and
Symptom Ontology (SYMP). As an example, CHEBI, FMA, is used as explained in
Section 4.3.

DSM-V (Diagnostic and Statistical Manual of Mental
Disorders)242424https://www.psychiatry.org/psychiatrists/practice/dsm
references more than 70 mental disorders that complement the International
Classification of Diseases (ICD). DSM-V helps clinicians and researchers
define and classify mental disorders, which can improve diagnoses, treatment,
and research. DSM-V provides a checklist form of symptoms for better
diagnosis. As an example, DSM-V is used as explained in Section 5.3.

Mental Health Ontology (Hadzic ., 2008) comprises three sub-ontologies: 1)
disorder/illness types, (2) factors, and (3) treatments. Disorders types are
anxiety disorder, eating disorder, childhood disorder, cognitive disorder,
mood disorder. Factors can be: 1) Physical (e.g. vitamin B deficiency, health
injury, liver disease), 2) Environmental (E physical, social, financial), 3)
Personal (belief, emotion, response). The need to understand emotions like
stress, anger, bitterness, guilt, joy, happiness, peace, and fear, since they
directly affect mental health is highlighted by Hadzic . (2008).

Mental Functioning (MF) Ontology and Mental Disease (MD)
Ontology252525https://obofoundry.org/ontology/mfomd.html (Hastings ., 2012)
describe human mental functioning and disease, including mental processes such
as cognitive processes and qualities such as intelligence. MF Ontology is
based on Basic Formal Ontology (BFO). MD ontology covers concepts such as
disease, diagnosis, disorder, and addiction. Mental Functioning Ontology and
Mental Disease Ontology could be used to map answers from clinical interview
questionnaires about mood, psychotic disorders, and related spectrum
conditions. Hastings et al. also designed emotion ontology. Mapping to
https://obofoundry.org/ontology/mfomd.html (Hastings ., 2012) is not simple
since there are no labels or comments within the ontology code but concepts
IRI such as MFOMD_0000040. Fortunately, the URI are deferenceable which means
that if we copy and paste
https://ontobee.org/ontology/MFOMD?iri=http://purl.obolibrary.org/obo/MFOMD_0000040
we can get additional information such as the definition: "A diagnosis
asserting the presence of an instance of a mental disease in a given
organism."

Depression KG (Huang ., 2017) is a disease-centric KG applied to Major
Depressive Disorder, which addresses several challenges: (1) Heterogeneity of
datasets, (2) text processing, (3) incompleteness, inconsistency, and
incorrectness of datasets, and (4) expressive, representation of medical
knowledge. Depression KG utilizes rule-based reasoning over the KG, which
helps psychiatric doctors without KG expertise. MDepressionKG (Fu ., 2021)
integrates the human microbial metabolism network, human diseases, microbes
and other ontologies. Ontology for College Student Mental Health Service
(CSMH) (X. Zhang Chen, 2020) describes appointments, mental disorders, self-
help resources, information for parents, local referral sources, and substance
abuse prevention. Some of the information is extracted from two CSMH websites.
Ontology for mental disorders - Schizophrenia Spectrum Ontology (Amoretti .,
2019), is compliant with DSM-5 descriptions of mental disorders, with a
specific focus on Schizophrenia. It comprises 58 classes (Mental_Disorder,
Patient, and Symptom), 5 properties, and 191 axioms. Classes of the
Schizophrenia Spectrum category and the associated symptoms are defined.
Future work is planned to address borderline personality disorder or major
depression. Ontology for managing mental healthcare network in Brazil (Yamada
., 2018), based on BFO, used for integration and interoperability between
databases and to design a Semantic Web-based Decision Support System (DSS) for
a regional mental healthcare network in Brazil for clinical and administrative
processes. The challenges of dealing with standardization and low-quality data
are highlighted. The competency question is whether a manager wants to know
how many people had schizophrenia in the city of São Paulo in 2017 without
looking at various systems of hospitals in the city.

###  4.2 Mapping to Standards: ETSI SmartM2M SAREF4EHAW

The mapping to the ETSI SmartM2M SAREF4EHAW ontology is already explained
within the book chapter "SAREF4EHAW-Compliant Knowledge Discovery and
Reasoning for IoT-based Preventive Healthcare and Well-Being" (Gyrard Kung,
2022). The mapping focuses on the sensor type used.

###  4.3 Mapping to Standardized Health KGs/Ontologies/Terminologies: SNOMED-
CT, FMA, RXNORM, MedDRA, LOINC, ChEBI, MESH, GALEN and DBpedia

The mapping to health knowledge bases is explained within "Interdisciplinary
IoT and Emotion Knowledge Graph-Based Recommendation System to Boost Mental
Health" (Gyrard Boudaoud, 2022). We mapped hormones and neurotransmitters
concepts. We searched key ontologies on the Bioportal ontology catalog, to be
mapped with the Emotion KG. We found ontologies such as SNOMED-CT, Mapping
Foundational Model of Anatomy (FMA), RXNORM, MedDRA, Logical Observation
Identifier Names and Code (LOINC), Medical Subject Headings (MESH), GALEN, and
Chemical Entities of Biological Interest Ontology (ChEBI). The mappings of
hormones and neurotransmitters are summarized in two Tables "Subset of mapping
hormones and neurotransmitters to existing knowledge bases to demonstrate the
difficulty of reusing only one knowledge base." DBpedia is also used due to
its popularity, and links emotion-related concepts to existing emotion
ontologies when available online. Most of the emotion ontologies cannot be
found on BioPortal; only Hastings’s ontology (Hastings ., 2011) is referenced
on BioPortal.

##  5 Ontology-Based Mental Health Recommender System and Project Use Cases:
ACCRA, etc.

Project use cases are explained in this Section 5. Social robots to support
active and healthy aging (ACCRA European-Japan Project) in Section 5.1, Large
Language Models (LLMs) for Mental Health in Section 5.2, and other projects on
Mental Health such as Depression and Suicide in Section 5.3.

###  5.1 ACCRA European-Japan Project: Social robots to support active and
healthy aging

To design emotional-based robotic applications, how can Internet of Robotic
Things (IoRT) technology and co-creation methodologies be used? The ACCRA
(Agile Co-Creation of Robots for Ageing) EU project262626https://www.accra-
project.org/en/sample-page/ (Gyrard ., 2021), coordinated by Trialog, develops
advanced social robots to support active and healthy aging, co-created by
various stakeholders such as aging people and physicians. Three robots, Buddy,
ASTRO, and RoboHon, are used for daily life, mobility, and conversation. The
three robots understand and convey emotions in real-time using the Internet of
Things and Artificial Intelligence technologies (e.g., knowledge-based
reasoning). The ACCRA project explains that social companion robots assist
elderly people in staying independent at home and decrease their social
isolation. A challenge was to design applications usable by elderly people
using co-creation methodologies involving multiple stakeholders and a
multidisciplinary research team (e.g., elderly people, medical professionals,
and computer scientists such as roboticists or IoT engineers).

###  5.2 Large Language Models (LLMs) for Mental Health

Large Language Models (LLMs) have become increasingly popular due to their
vast statistical knowledge, allowing them to produce fluent English sentences
and exhibit human-like performance across tasks such as question-answering,
summarization, and recommendations. The debut of ChatGPT on November 30, 2022,
garnered considerable attention alongside similar autoregressive LLMs like
Google BARD, Google GEMINI, and Anthropic Claude (Minaee ., 2024). Despite
their impressive performance, these LLMs have been criticized for providing
confidently asserted yet factually inaccurate information, referred to as
“hallucination” (Rawte ., 2023). This poses a significant challenge to their
reliability and trustworthiness. Moreover, LLMs sometimes yield inconsistent
answers, eroding trust in their outputs. Attention explanations (generated by
models) do not closely align with the ground truth explanations provided by
human experts (Mohammadi ., 2024). When LLMs offer irrelevant explanations, it
exacerbates trust issues, casting doubt on their reliability as tools (Y.
Zhang ., 2023). Consequently, their application in healthcare, particularly
mental health, has been hindered. Although there are mental health-specific
LLMs, their performance lacks consistency, reliability, explainability, and
trust assessment (Gaur Sheth, 2024).

Efforts to enhance these LLMs for better utility in healthcare have led to
recent research focusing on instruction-tuned and retrieval-augmented (e.g.
Tilwani . (2024)) LLMs (Lewis ., 2020). Instruction tuning involves
incorporating an additional feature called “instruction” into the dataset,
which can be a guideline, protocol, or rule for the LLM to follow (S. Zhang .,
2023; Sheth ., 2022). However, such training methods have not proven very
effective in sensitive domains. Recent work has shown that rules learned by
LLMs after instruction tuning do not match the model’s performance level,
raising concerns about this training approach, particularly in areas akin to
moral question answering, such as mental health. An empirical study by Gupta .
(2022) demonstrates that LLMs struggle to complete clinical questionnaires for
depression and anxiety . Subsequently, Roy, Zi . (2023) proposed architecture
changes, particularly in autoregressive language models, suggesting that a
tree-based learning prediction layer could yield safer outcomes in mental
health contexts . Further exploration at the intersection of instruction-tuned
LLMs and mental health is warranted. Examples of publicly available mental
health LLMs include ChatCBPTSD, Diagnosis of Thought Prompting (Z. Chen .,
2023), Mental-LLM (Alpaca/FLAN-T5 based), MentaLLaMA (LLaMA-2 based; (K. Yang
., 2023)), ChatCounselo, ExTES-LLaMA (both LLaMA based), and BBMHR
(BlenderBot-BST based), while some, like MindShift, Psy-LLM, and LLM-
Counselors (all GPT-3.5 based), remain unavailable to the public (Hua .,
2024).

Retrieval-augmented LLMs represent another category, where a generator model
is paired with a knowledge retriever capable of accessing documents from a
vectorized database. These LLMs draw context from the retriever, offering
reliability and domain-specific explainability crucial in domains like mental
health. Gaur . (2022) demonstrated the extension of these LLMs to knowledge
graphs . However, experimentation has primarily been limited to open-domain
knowledge-intensive language understanding tasks, leaving its utility in
mental health as an ongoing research question (Sarkar ., 2023). An example of
retrieval-augmented LLM is shown in openCHA (Z. Yang ., 2024). Abbasian .
(2023) developed openCHA, a framework that empowers health agents to enhance
the processing of healthcare inquiries by efficiently analyzing input queries,
integrating essential information, and offering personalized, context-aware
responses.The framework’s effectiveness in managing complex healthcare tasks
through various expert-provided demonstrations. For example, Z. Yang . (2024)
leveraged openCHA to create a personalized nutrition-oriented food
recommendation chatbot, enabled by user’s longitudinal data on diabetes,
American Diabetes Association dietary guidelines, the Nutritionix information,
personal causal models, and population models. On an evaluation includes 100
diabetes-related questions on daily meal choices and the potential risks
associated with the diet, openCHA demonstrated superior performance compared
to state-of-the-art GPT 4.

###  5.3 Other Projects on Mental Health

Mental health professionals (MHPs) are overwhelmed by the rising prevalence of
declining mental health (MH), depression, and suicide risk. Traditionally,
they rely on time-consuming clinical questionnaires (e.g., DSM-5 assessment
measures, strengths and difficulties questionnaires) and long patient
interviews272727https://wiki.aiisc.ai/index.php?title=Mental_Health_Projects.
However, the growing demand for MH services and the shortage of MHPs motivate
automated methods for early screening. However, it’s important to note that
receiving high-quality assistance is not
guaranteed282828https://wiki.aiisc.ai/index.php?title=Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression.
We require online screening assistance as an improvement over online
consultations (e.g., BetterHelp), aiming for higher-quality support. AI has
emerged as a promising tool for analyzing text data from various sources,
including Electronic Health Records (EHR) and social media posts (T. Zhang .,
2022; Joyce ., 2023; Thiruvalluru ., 2021).

Research in AI and Mental health can be broadly categorized into two
categories: (a) Statistical data-driven machine learning in the realm of
mental health, as exemplified by the contributions of De Choudhury . (2013),
De Choudhury . (2016), Saha . (2019), Chancellor De Choudhury (2020), Shing .
(2018) and Gkotsis . (2017), represents a significant advancement. However,
while these studies showcase the effectiveness of AI in this domain, they are
limited in their ability to instill trustworthiness, primarily due to a
deficiency in explainability and interoperability, which could be greatly
enhanced by incorporating domain-specific expertise. (b) Knowledge-driven
Machine Learning for Mental Health: This comprises work involving clinical
questionnaires for question answering, summarization, longitudinal assessment
(Alambo ., 2019; Gupta ., 2022; Gaur, Aribandi ., 2021; Manas ., 2021), a
diagnostic statistical manual for mental health disorders for identifying
mental health disorders (Gaur ., 2018), and detecting and assessing the
severity of substance use disorder using domain-specific drug abuse ontology
(Kursuncu ., 2018; Lokala, Srivastava ., 2022; Lokala, Lamy ., 2022). Machine-
readable mental health knowledge has resulted in explainable classification
and interpretable design of black-box language models and conversational
agents (Gaur, Faldu Sheth, 2021; Dalal ., 2024; Roy, Sheth Gaur, 2023).
Further, datasets designed using such a knowledge are capable of examining
grounding, instructability, and alignment of domain-specific language models
(e.g., ClinicalBERT (Alsentzer ., 2019), PsychBERT (Vajre ., 2021), MentalBERT
(Ji ., 2021)) to the intent, needs, and requirements of MHPs (Sheth ., 2021;
Gaur ., 2019).

##  6 Conclusion and Future Work

A Mental Health KG (ontology and dataset) acquires knowledge from ontology-
based projects classified within the LOV4IoT ontology catalog (Depression,
Mental Health, and Emotion). LOV4IoT supports researchers with 1) the
Systematic Literature Survey, which is a time-consuming task and requires an
eagerness to learn and investigate existing projects, 2) FAIR principles to
encourage researchers to share their reproducible experiments by publishing
online ontologies, datasets, rules, etc.

Short-term challenges: LOV4IoT is relevant for the IoT community. The results
are encouraging to update the dataset with additional domains and ontologies.
LOV4IoT leads to the AIOTI (The Alliance for the Internet of Things
Innovation) IoT ontology landscape survey
form292929https://ec.europa.eu/eusurvey/runner/OntologyLandscapeTemplate and
analysis result303030https://bit.ly/3fRpQUU, executed by the Standard WG -
Semantic Interoperability Expert Group. It aims to help industrial
practitioners and non-experts answer those questions: Which ontologies are
relevant in a certain domain? Where to find them? How to choose the most
suitable? Who is maintaining and taking care of their evolution? There is also
the AIOTI Health WG white paper publications on health data space "IoT/Edge
Computing and Health Data and Data Spaces"313131https://aioti.eu/aioti-white-
paper-iot-edge-computing-and-health-data-and-data-spaces/, "AI for better
health"323232https://aioti.eu/aioti-wg-health-white-paper-on-ai-for-better-
health/, and "IoT Improving Healthy Urban Living"333333https://aioti.eu/wp-
content/uploads/2022/09/IoT-and-Healthy-Urban-Living-Final.pdf.

Mid-term challenges: Automatic knowledge extraction from ontologies and
scientific publications describing the ontology purpose is challenging, as
highlighted in our AI4EU Knowledge Extraction for the Web of Things (KE4WoT)
Challenge. The challenge encourages the reuse of the expertise designed by
domain experts and makes the domain knowledge usable, interoperable, and
integrated by machines. We released the set of ontologies, as dumps, web
services, and tutorials, and made them available.

Long-term challenges: To improve the veracity and the evaluation of the KG
integrated with a reasoning engine, involving domain experts such as
psychologists, neuroscientists, etc. would enhance the KG, by proving more of
the facts. The knowledge-based reasoning engine can be extended by considering
additional research fields such as psychophysiology, psychobiology, etc. An
emphasis on the emotional aspect can be done (e.g., fear, pessimism, sadness)
since it impacts mental health.

## Acknowledgements and Funding

We want to acknowledge the Kno.e.sis research team (lead by Professor Amit
Sheth) from Wright State University, Ohio, USA for fruitful discussions about
related topics such as "Mental Health/Depression/Suicide", and "Semantic,
Cognitive, and Perceptual Computing" and with cognitive psychologists such as
Professor Valerie Shalin during Dr. Gyrard’s post-doc in 2018-2019.

This work has partially received funding from the European Union’s Horizon
2020 research and innovation program under project grant agreement StandICT.eu
2026 No. 101091933 (open call). We would like to thank the project partners
for their valuable comments. The opinions expressed are those of the authors
and do not reflect those of the sponsors.

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