From Machine Health to Elderly Health: A Sustainability-oriented Elderly-centric Social Robot System enabled by PHM
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https://orcid.org/0000-0001-6426-7124
Abstract
The purpose of designing machines is to serve humans and assist them in completing their work on time. A machine, such as a social robot, can be particularly supportive for older individuals who may have reduced strength and be more susceptible to various health issues. The present paper proposes a scenario in which elderly health is monitored by a social robot. As an observer, a social robot, through perceiving its environment, can capture several moments (including daily activities or falls) of an older adult and can provide timely reminders and alerts to the elderly and their caregivers, ensuring the elderly's well-being. Therefore, validation of the machine's health (functioning) is necessary. To accomplish this, the current paper suggests utilization of Prognosis and Health Management (PHM) for the elderly-centric robot system. Furthermore, a PHM-enabled elderly-centric robot system has two main entities: an older adult and a robot, hence it is important to analyze the sustainability dimensions from various perspectives. There are two main objectives of this paper: (i) to develop a comprehensive list of sustainability topics under various dimensions achieved from the examination of three sustainable frameworks: the Triple Bottom Line, Responsible Research and Innovation, Sustainable Assessment and Sustainability Awareness Framework (SuSAF). (ii) to apply the comprehensive list of sustainability dimensions to the proposed case of PHM-enabled elderly-centric social robot system. The results suggest that SuSAF is the most comprehensive and suitable framework for the sustainability assessment of the proposed system. Furthermore, the use of sustainable dimensions can ensure improved robot health and, hence, the health of the elderly.
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Social Robot, Sustainability, Elderly Health, PHM
Brundtland, G., M. Khalid, S. Agnelli, S. Al-Athel, B. Chidzero, L. Fadika, V. Hauff, I. Lang, M. Shijun and M. M. de Botero (1987). "Our common future (Brundtland report)." Journal Medicine and War 4(1).
Cantone, A. A., M. Esposito, F. P. Perillo, M. Romano, M. Sebillo and G. Vitiello (2023). "Enhancing elderly health monitoring: Achieving autonomous and secure living through the integration of artificial intelligence, autonomous robots, and sensors." Electronics 12(18): 3918.
Cheng, S., M. H. Azarian and M. G. Pecht (2010). "Sensor systems for prognostics and health management." Sensors 10(6): 5774–5797.
Duboc, L., S. Betz, B. Penzenstadler, S. A. Kocak, R. Chitchyan, O. Leifler, J. Porras, N. Seyff and C. C. Venters (2019). Do we Really Know What we are Building? Raising Awareness of Potential Sustainability Effects of Software Systems in Requirements Engineering. 2019 IEEE 27th International Requirements Engineering Conference (RE).
Ehlers, M.-H., N. El Benni and M. Douziech (2025). "Implementing responsible research and innovation and sustainability assessment in research projects: A framework and application." Research Policy 54(2): 105164.
Finkbeiner, M., A. Inaba, R. Tan, K. Christiansen and H.-J. Klüppel (2006). "The new international standards for life cycle assessment: ISO 14040 and ISO 14044." The international journal of life cycle assessment 11(2): 80–85.
Hu, Y., X. Miao, Y. Si, E. Pan and E. Zio (2022). "Prognostics and health management: A review from the perspectives of design, development and decision." Reliability Engineering & System Safety 217: 108063.
Kusmendar, A. M. S. Asih and N. M. E. Normasari (2025). "Exploring sustainable pathways: A systematic literature review of three pillars of sustainability applications." Sustainable Futures 10: 100970.
Liu, Z., Z. Jia, C.-M. Vong, J. Han, C. Yan and M. Pecht (2018). "A patent analysis of prognostics and health management (PHM) innovations for electrical systems." IEEE Access 6: 18088–18107.
Mardini, M. T., Y. Iraqi and N. Agoulmine (2019). "A survey of healthcare monitoring systems for chronically ill patients and elderly." Journal of medical systems 43(3): 50.
Pantelopoulos, A. and N. G. Bourbakis (2010). "Prognosis—a wearable health-monitoring system for people at risk: Methodology and modeling." IEEE Transactions on Information Technology in Biomedicine 14(3): 613–621.
Robert, K. W., T. M. Parris and A. A. Leiserowitz (2005). "What is sustainable development? Goals, indicators, values, and practice." Environment: science and policy for sustainable development 47(3): 8–21.
Serrano-Arcos, M. M., B. Payán-Sánchez and A. Labella-Fernández (2021). 3 - Evolution and trends of sustainable approaches.
Sustainable Resource Management. C. M. Hussain and J. F. Velasco-Muñoz, Elsevier: 51–73.
Slaper, T. F. and T. J. Hall (2011). "The triple bottom line: What is it and how does it work." Indiana business review 86(1): 4–8.
Su, H. and J. Lee (2023). "Machine learning approaches for diagnostics and prognostics of industrial systems using open source data from PHM data challenges: a review." arXiv preprint arXiv:2312.16810.
Swarr, T. E., D. Hunkeler, W. Klöpffer, H.-L. Pesonen, A. Ciroth, A. C. Brent and R. Pagan (2011). "Environmental life-cycle costing: a code of practice." The International Journal of Life Cycle Assessment 16(5): 389–391.
Tavanti, M. (2025). Triple Bottom Line (TBL). Sustainability in Business Management, Volume 1: A Machine-Generated Summary Covering Foundations and Strategies, Springer: 33–63.
UNEP (2020). "Guidelines for Social Life Cycle Assessment of Products and Organisations 2020—Life Cycle Initiative."Yang, L. (2023). "Data monitoring for a physical health system of elderly people using smart sensing technology." Wireless networks 29(8): 3665–3678.
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