Smart Wearable Technologies for Autonomous Mental Health Monitoring in the Elderly: A Systematic Review and Design Perspectives

Authors

DOI:

https://doi.org/10.4108/eetpht.11.10883

Keywords:

Smart Wearables, Emotion Recognition, Autonomous Mental Health Monitoring, Depression Detection, Elderly Mental Health

Abstract

INTRODUCTION: The increasing prevalence of mental health issues among older adults has generated interest in smart wearable technologies as tools for emotion recognition and depression monitoring. However, their application in ageing populations remains underexplored, and there is no established set of design guidelines tailored to the needs and contexts of older users.

OBJECTIVES: This paper aims to review current research on wearable technologies for mental health monitoring in older adults and to identify key design considerations to inform future development.

METHODS: A systematic literature review was conducted following PRISMA 2020 guidelines. Studies from 1988 to 2025 were included if they examined the use of smart wearables for detecting emotional or depressive states in older adults, or if broader age ranges were analysed in ways that explicitly addressed ageing-related factors or design considerations.

RESULTS: The review revealed notable advances in sensor-based and contactless emotion recognition. However, most systems lacked empirical validation with older users, and usability, privacy, and ethical concerns were frequently unaddressed. Few studies adopted age-specific methodologies or considered the cognitive and physical characteristics of older adults.

CONCLUSION: While wearable technologies show potential for supporting autonomous mental health care in older adults, their effectiveness depends on user-centred and ethically responsible design. This paper identifies the absence of standardised guidelines and outlines preliminary principles to inform future interdisciplinary work. Given the limited number of eligible studies involving older adults, the findings should be considered exploratory and indicative rather than generalisable across broader ageing populations.

Downloads

Download data is not yet available.

References

[1] Blazer DG. Depression in late life: review and commentary. J Gerontol A Biol Sci Med Sci. 2003 Mar;58(3):249–65.

[2] Hasin D, Link B. Age and recognition of depression: implications for a cohort effect in major depression. Psychological Medicine. 1988 Aug;18(3):683–8.

[3] Lee S, Kim H, Park MJ, Jeon HJ. Current Advances in Wearable Devices and Their Sensors in Patients With Depression. Front Psychiatry. 2021 June 17;12:672347.

[4] Wang R, Aung MSH, Abdullah S, Brian R, Campbell AT, Choudhury T, et al. CrossCheck: toward passive sensing and detection of mental health changes in people with schizophrenia. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing [Internet]. New York, NY, USA: Association for Computing Machinery; 2016 [cited 2025 June 17]. p. 886–97. (UbiComp ’16). Available from: https://dl.acm.org/doi/10.1145/2971648.2971740

[5] Sheikh M, Qassem M, Kyriacou PA. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Front Digit Health. 2021 Apr 7;3:662811.

[6] Chan M, Estève D, Fourniols JY, Escriba C, Campo E. Smart wearable systems: Current status and future challenges. Artificial Intelligence in Medicine. 2012 Nov 1;56(3):137–56.

[7] O’Sullivan G, Whelan B, Gallagher N, Doyle P, Smyth S, Murphy K, et al. Challenges of using a Fitbit smart wearable among people with dementia. Int J Geriat Psychiatry. 2023 Mar;38(3):e5898.

[8] Ko BC. A Brief Review of Facial Emotion Recognition Based on Visual Information. Sensors. 2018 Feb;18(2):401.

[9] Glisky EL. Changes in Cognitive Function in Human Aging. In: Riddle DR, editor. Brain Aging: Models, Methods, and Mechanisms [Internet]. Boca Raton (FL): CRC Press/Taylor & Francis; 2007 [cited 2025 June 17]. (Frontiers in Neuroscience). Available from: http://www.ncbi.nlm.nih.gov/books/NBK3885/

[10] Murman DL. The Impact of Age on Cognition. Semin Hear. 2015 Aug;36(3):111–21.

[11] Setz C, Arnrich B, Schumm J, La Marca R, Tröster G, Ehlert U. Discriminating stress from cognitive load using a wearable EDA device. IEEE Trans Inf Technol Biomed. 2010 Mar;14(2):410–7.

[12] Gjoreski M, Luštrek M, Gams M, Gjoreski H. Monitoring stress with a wrist device using context. Journal of Biomedical Informatics. 2017 Sept 1;73:159–70.

[13] Valenza G, Citi L, Lanatá A, Scilingo EP, Barbieri R. Revealing real-time emotional responses: a personalized assessment based on heartbeat dynamics. Sci Rep. 2014 May 21;4:4998.

[14] Peng R, Guo Y, Zhang C, Li X, Huang J, Chen X, et al. Internet-delivered psychological interventions for older adults with depression: A scoping review. Geriatric Nursing. 2024 Jan;55:97–104.

[15] Harte R, Glynn L, Rodríguez-Molinero A, Baker PM, Scharf T, Quinlan LR, et al. A Human-Centered Design Methodology to Enhance the Usability, Human Factors, and User Experience of Connected Health Systems: A Three-Phase Methodology. JMIR Hum Factors. 2017 Mar 16;4(1):e8.

[16] Partheniadis K, Stavrakis M. Design and evaluation of a digital wearable ring and a smartphone application to help monitor and manage the effects of Raynaud’s phenomenon. Multimed Tools Appl [Internet]. 2019 Feb 1 [cited 2018 Sept 3]; Available from: https://doi.org/10.1007/s11042-018-6514-3

[17] Partheniadis K, Stavrakis M. Designing a Smart Ring and a Smartphone Application to Help Monitor, Manage and Live Better with the Effects of Raynaud’s Phenomenon. In: Guidi B, Ricci L, Calafate C, Gaggi O, Marquez-Barja J, editors. Smart Objects and Technologies for Social Good. Springer International Publishing; 2018. p. 1–10. (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering).

[18] Alarcão SM, Fonseca MJ. Emotions Recognition Using EEG Signals: A Survey. IEEE Trans Affect Comput. 2019 July 1;10(3):374–93.

[19] Keenan AJ, Tsourtos G, Tieman J. The Value of Applying Ethical Principles in Telehealth Practices: Systematic Review. J Med Internet Res. 2021 Mar 30;23(3):e25698.

[20] García-Hernández RA, Luna-García H, Celaya-Padilla JM, García-Hernández A, Reveles-Gómez LC, Flores-Chaires LA, et al. A Systematic Literature Review of Modalities, Trends, and Limitations in Emotion Recognition, Affective Computing, and Sentiment Analysis. Applied Sciences. 2024 Aug 15;14(16):7165.

[21] Li J, Ma Q, Chan AHs, Man SS. Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Applied Ergonomics. 2019 Feb;75:162–9.

[22] Hickey BA, Chalmers T, Newton P, Lin CT, Sibbritt D, McLachlan CS, et al. Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review. Sensors. 2021 May 16;21(10):3461.

[23] Moore K, O’Shea E, Kenny L, Barton J, Tedesco S, Sica M, et al. Older Adults’ Experiences With Using Wearable Devices: Qualitative Systematic Review and Meta-synthesis. JMIR Mhealth Uhealth. 2021 June 3;9(6):e23832.

[24] Li J, Ma Q, Chan AHS, Man SS. Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Applied Ergonomics. 2019 Feb 1;75:162–9.

[25] Huang L, Hung K, Man-Tat Man G. Development a Low-cost Smart Eyewear System for Eye & Head Movement Measurement and Analysis. In: 2023 8th International Conference on Instrumentation, Control, and Automation (ICA). 2023. p. 64–9.

[26] Gomes N, Pato M, Lourenço AR, Datia N. A Survey on Wearable Sensors for Mental Health Monitoring. Sensors. 2023 Jan 25;23(3):1330.

[27] Holthe T, Halvorsrud L, Karterud D, Hoel KA, Lund A. Usability and acceptability of technology for community-dwelling older adults with mild cognitive impairment and dementia: a systematic literature review. Clin Interv Aging. 2018;13:863–86.

[28] Mughal F, Raffe W, Garcia J. Emotion Recognition Techniques for Geriatric Users: A Snapshot. In: 2020 IEEE 8th International Conference on Serious Games and Applications for Health (SeGAH). 2020. p. 1–8.

[29] Kim H, Lee S, Lee S, Hong S, Kang H, Kim N. Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone. JMIR Mhealth Uhealth. 2019 Oct 16;7(10):e14149.

[30] Choi J, Lee S, Kim S, Kim D, Kim H. Depressed Mood Prediction of Elderly People with a Wearable Band. Sensors (Basel). 2022 May 31;22(11).

[31] Mishra R, Park C, York MK, Kunik ME, Wung SF, Naik AD, et al. Decrease in Mobility during the COVID-19 Pandemic and Its Association with Increase in Depression among Older Adults: A Longitudinal Remote Mobility Monitoring Using a Wearable Sensor. Sensors (Basel). 2021 Apr 29;21(9).

[32] Chen WL, Chen LB, Chang WJ, Tang JJ. An IoT-based elderly behavioral difference warning system. In: 2018 IEEE International Conference on Applied System Invention (ICASI). 2018. p. 308–9.

[33] Gutierrez Maestro E, De Almeida TR, Schaffernicht E, Martinez Mozos Ó. Wearable-Based Intelligent Emotion Monitoring in Older Adults during Daily Life Activities. Applied Sciences. 2023 May 3;13(9):5637.

[34] Onim MdSH, Kiselica A, Thapliyal H. Emotion Detection in Older Adults Using Physiological Signals from Wearable Sensors. In 2025. p. 990–5. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-105017579226&doi=10.1145%2F3716368.3735280&partnerID=40&md5=7656a11dc3ccf80dd75d46435458fe98

[35] Jiang Z, Lu L, Huang X, Tan C. Design of wearable home health care system with emotion recognition function. In: 2011 International Conference on Electrical and Control Engineering [Internet]. 2011. p. 2995–8. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-80955172038&doi=10.1109%2FICECENG.2011.6057832&partnerID=40&md5=069927456a44b0cb8520f9eb8b9ec58e

[36] Albites-Sanabria J, Palmerini L, Bandinelli S, Chiari L. Can Motor Outcomes Extracted from Wearables Inform on Non-Motor Clinical Outcomes? The Case of Sensor-Derived Turning Information. In: 2025 IEEE International Conference on Digital Health (ICDH). 2025. p. 175–80.

[37] Siddiqui S, Khan AA, Nait-Abdesselam F, Dey I. Anxiety and Depression Management For Elderly Using Internet of Things and Symphonic Melodies. In: ICC 2021 - IEEE International Conference on Communications. 2021. p. 1–6.

[38] Ma Y, Yin K. EMO-Care: Emotional Interaction System Based on Multimodal Fusion and Edge Intelligence for Emotional Care. In: 2024 4th International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI). 2024. p. 32–5.

[39] Suzuki K, Iguchi T, Nakagawa Y, Sugaya M. A multi-modal interaction robot based on emotion estimation method using physiological signals applied for elderly*. In: 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 2023. p. 2051–7.

[40] Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to Meta‐Analysis [Internet]. 1st ed. Wiley; 2009 [cited 2025 July 11]. Available from: https://onlinelibrary.wiley.com/doi/book/10.1002/9780470743386

[41] Wells GA, Shea B, O’Connell D, Peterson J, Welch V, Losos M, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. 2000 [cited 2025 June 17]; Available from: https://scholar.archive.org/work/zuw33wskgzf4bceqgi7opslsre/access/wayback/http://www3.med.unipmn.it/dispense_ebm/2009-2010/Corso%20Perfezionamento%20EBM_Faggiano/NOS_oxford.pdf

[42] McPheeters ML, Kripalani S, Peterson NB, Idowu RT, Jerome RN, Potter SA, et al. Closing the quality gap: revisiting the state of the science (vol. 3: quality improvement interventions to address health disparities). Evid Rep Technol Assess (Full Rep). 2012 Aug;(208.3):1–475.

[43] Munn Z, Moola S, Lisy K, Riitano D, Tufanaru C. Methodological guidance for systematic reviews of observational epidemiological studies reporting prevalence and cumulative incidence data. Int J Evid Based Healthc. 2015 Sept;13(3):147–53.

[44] Peek STM, Wouters EJM, van Hoof J, Luijkx KG, Boeije HR, Vrijhoef HJM. Factors influencing acceptance of technology for aging in place: a systematic review. Int J Med Inform. 2014 Apr;83(4):235–48.

[45] Vines J, Pritchard G, Wright P, Olivier P, Brittain K. An Age-Old Problem: Examining the Discourses of Ageing in HCI and Strategies for Future Research. ACM Trans Comput-Hum Interact. 2015 Feb 17;22(1):2:1-2:27.

[46] Tsiantzi K, Stavrakis M. The role of micro-interactions in patient use of medication monitoring control devices and smart packaging. In: Proceedings of the 5th European Conference on Design4Health. Sheffield, UK: Sheffield Hallam University; 2018.

[47] Wilkowska W, Ziefle M. Privacy and data security in E-health: Requirements from the user’s perspective. Health Informatics J. 2012 Sept 1;18(3):191–201.

[48] Sas C, Chopra R. MeditAid: a wearable adaptive neurofeedback-based system for training mindfulness state. Pers Ubiquit Comput. 2015 Oct 1;19(7):1169–82.

[49] Lu L, Zhang J, Xie Y, Gao F, Xu S, Wu X, et al. Wearable Health Devices in Health Care: Narrative Systematic Review. JMIR Mhealth Uhealth. 2020 Nov 9;8(11):e18907.

[50] Mittelstadt BD, Allo P, Taddeo M, Wachter S, Floridi L. The ethics of algorithms: Mapping the debate. Big Data & Society. 2016 Dec 1;3(2):2053951716679679.

[51] Lazar A, Thompson HJ, Demiris G. Design Recommendations for Recreational Systems Involving Older Adults Living With Dementia. J Appl Gerontol. 2018 May;37(5):595–619.

[52] Kordatos G, Stavrakis M. Design and evaluation of a wearable system to increase adherence to rehabilitation programmes in acute cruciate ligament (CL) rupture. Multimed Tools Appl. 2020 Dec;79(45–46):33549–74.

[53] Kordatos G, Stavrakis M. Preliminary design of a wearable system to increase adherence to rehabilitation programmes in acute Cruciate Ligament (CL) rupture. In: Proceedings of the 4th International Conference on Smart Objects and Technologies for Social Good (Goodtechs ’18). Bologna, Italy: EAI, ACM; 2018.

[54] Calvo RA, Milne DN, Hussain MS, Christensen H. Natural language processing in mental health applications using non-clinical texts. Nat Lang Eng. 2017 Sept;23(5):649–85.

[55] Schmidt P, Reiss A, Duerichen R, Laerhoven KV. Wearable affect and stress recognition: A review [Internet]. arXiv; 2018 [cited 2025 June 30]. Available from: http://arxiv.org/abs/1811.08854

Downloads

Published

07-01-2026

How to Cite

1.
Vogka N, Stavrakis M. Smart Wearable Technologies for Autonomous Mental Health Monitoring in the Elderly: A Systematic Review and Design Perspectives. EAI Endorsed Trans Perv Health Tech [Internet]. 2026 Jan. 7 [cited 2026 Jan. 8];11. Available from: https://publications.eai.eu/index.php/phat/article/view/10883