Implementation of Multimodal LLM Agents and Biomechanical Analysis for Remote Elderly Healthcare in Taiwan: A Case Study
Article Sidebar
Main Article Content
Abstract
Taiwan’s rapidly aging population and the unequal distribution of healthcare resources in remote areas pose a critical challenge to equitable medical services. This study proposes a multimodal AI system that integrates large language model (LLM) agents with real-time biomechanical gesture recognition, designed for remote elderly healthcare. By combining browser-based joint-angle tracking with cloud-hosted, context-aware conversational agents, the system enables natural language communication and clinical motion analysis without transmitting video data. Evaluated in rehabilitation scenarios in Taiwan, the platform demonstrates low latency, accurate pose classification, and automated clinical scoring using the Brunnstrom and Fugl–Meyer scales. Its multilingual interface supports personalized, secure, and efficient health interactions, aligning with Taiwan’s Long-Term Care Plan 2.0 and AI healthcare initiatives. This solution addresses communication and mobility barriers, enhances healthcare accessibility in rural areas, and contributes to global discourse on ethical, inclusive, and context-aware AI in eldercare.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Supply Chain and Sustainability Research uses a Gold Open Access model. All articles at Supply Chain and Sustainability Research are published Open Access. Publication is funded by a fee paid at the time of acceptance (Publication Fee). From June 2020 onward, author(s) retain copyright of their work, with articles licensed to the publisher Southeast Bangkok College and Supply Chain and Sustainability Research. All articles published on this site use the Creative Commons Attribution 4.0 License (CC BY 4.0). You do not need to seek permission from Supply Chain and Sustainability Research or Southeast Bangkok College for reuse of contents published on this site.
This means;
All articles are immediately available free-of-charge upon publication.
Copyright on all Open Access articles in Supply Chain and Sustainability Research is retained by the author(s), or the author's Employer.
Author(s) grant Southeast Bangkok College a license to publish the article and identify itself as the original publisher.
Author(s) also grant any third party the right to use the article freely as long as its integrity is maintained and the original authors, citation details and publisher are identified.
You are free to:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
There may be exceptions to copyright and licensing for articles which were previously published under policies that are different from the above, in this case different licensing conditions may then apply. If in any case Supply Chain and Sustainability Research contains material republished with permission under a different license, you may need to seek permission for reuse from the copyright holder. In all such cases, however, access to these articles is free from fees or any other access restrictions.
Open Access ensures the widest possible access to research, makes research available to wider audiences, allows readers to use articles and data, and also allows author(s) to distribute their works freely. Open Access accelerates research by removing barriers to collaboration and accelerates scientific communication.
If you require more information, please don't be hesitated to contact the Editorial team anytime here or contact the Editorial Office below.
References
Abbas, M., Saleh, M., Somme, D., & Le Bouquin Jeannès, R. (2023). Data-driven systems to detect physical weakening from daily routine: A pilot study on elderly over 80 years old. PloS one, 18(1), e0274306. https://doi.org/10.1371/journal.pone.0274306
Agustin, K., & Chou, S.-Y. (2019). Impact of an Ageing Society on Healthcare Expenditure of National Health Insurance in Taiwan. Jurnal Teknik Industri: Jurnal Keilmuan Dan Aplikasi Teknik Industri, 21(2), 49–56. https://doi.org/10.9744/jti.21.2.49-56
Brunnstrom, S. (1970). Movement therapy in hemiplegia: A neurophysiological approach. Harper & Row.
Casiez, G., Roussel, N., & Vogel, D. (2012). 1 € filter: A simple speed-based low-pass filter for noisy input in interactive systems. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2527–2530. https://doi.org/10.1145/2207676.2208639
Char, D. S., Shah, N. H., & Magnus, D. (2020). Implementing machine learning in health care—addressing ethical challenges. The New England Journal of Medicine, 378(11), 981-983. https://doi.org/10.1056/NEJMp1714229
Chiu, K.-H., & Yang, Y. Y. (2009). A case study of remote monitoring of health status of the elderly at home in Taiwan. Proceedings of the International Conference on e-Business (ICEB), 496–506.
Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98. https://doi.org/10.7861/futurehosp.6-2-94
Davico, G., Labanca, L., Gennarelli, I., Benedetti, M. G., & Viceconti, M. (2024). Towards a comprehensive biomechanical assessment of the elderly combining in vivo data and in silico methods. Frontiers in Bioengineering and Biotechnology, 12, 1356417. https://doi.org/10.3389/fbioe.2024.1356417
DelveInsight. (2025, June 4). Global AI in remote patient monitoring market to cross USD 13 billion by 2032 [Press release]. Globenewswire. https://www.globenewswire.com/news-release/2025/06/04/3093902/0/en/Global-AI-in-Remote-Patient-Monitoring-Market-to-Cross-USD-13-Billion-by-2032-DelveInsight.html
Fugl-Meyer, A. R., Jääskö, L., Leyman, I., Olsson, S., & Steglind, S. (1975). The post-stroke hemiplegic patient: A method for evaluation of physical performance. Scandinavian Journal of Rehabilitation Medicine, 7(1), 13–31.
Gottschlich, D., Saadati, N., Saadati, S. A., & Şahin, A. (2024). AI in Elderly Care: Understanding the Implications for Independence and Social Interaction. AI and Tech in Behavioral and Social Sciences, 2(3), 36-42. https://doi.org/10.61838/kman.aitech.2.3.5
Gu, F., Fan, J., Wang, Z., Liu, X., Yang, J., & Zhu, Q. (2023). Automatic range-of-motion measurement via smartphone images for telemedicine examination of the hand. Science Progress, 106(1), 1–12. https://doi.org/10.1177/00368504231152740
Iqbal, S. (2023). Artificial intelligence tools and applications for elderly healthcare – Review. In Proceedings of the 2023 9th International Conference on Computing and Artificial Intelligence (ICCAI '23) (pp. 394–397). Association for Computing Machinery. https://doi.org/10.1145/3594315.3594347
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2
Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G.C., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17.
Klakegg, S., van Berkel, N., Visuri, A., Huttunen, H. L., Hosio, S., Luo, C., Goncalves, J., & Ferreira, D. (2017). Designing a context-aware assistive infrastructure for elderly care. Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 451-456. https://doi.org/10.1145/3123024.3124403
Liu, X., Rivera, S. C., Moher, D., Calvert, M. J., & Denniston, A. K., for the SPIRIT-AI and CONSORT-AI Working Group. (2021). Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension. BMJ, 370, m3164. https://doi.org/10.1136/bmj.m3164
Ministry of Health and Welfare (MOHW), Taiwan. (2024, December 23). Long-term Care Plan 2.0. Executive Yuan. https://english.ey.gov.tw/News3/9E5540D592A5FECD/332a78c0-0c8e-4064-bd71-8c22477dae75
National Cheng Kung University (NCKU). (2025, April 15). How AI and robots are caring for the elderly in Taiwan’s ageing society. GovInsider Asia. https://govinsider.asia/intl-en/article/how-ai-and-robots-are-caring-for-the-elderly-in-taiwans-ageing-society-dr-jenny-su-ncku
National Development Council, Taiwan. (2022). Population Projections for Taiwan (2022–2070). Executive Yuan, R.O.C. (Taiwan).
OpenAI. (2023). GPT-4 technical report. https://arxiv.org/abs/2303.08774
Perimal, M., Basah, S. N., Safar, M. J. A., & Yazid, H. (2018). Hand-gesture recognition algorithm based on finger counting. Journal of Telecommunication, Electronic and Computer Engineering, 10(1-13), 19–25.
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28(1), 31-38. https://doi.org/10.1038/s41591-021-01614-0
Serrano, L. P., Maita, K. C., Avila, F. R., Torres-Guzman, R. A., Garcia, J. P., Eldaly, A. S., Haider, C. R., Felton, C. L., Paulson, M. R., Maniaci, M. J., & Forte, A. J. (2023). Benefits and Challenges of Remote Patient Monitoring as Perceived by Health Care Practitioners: A Systematic Review. The Permanente journal, 27(4), 100–111. https://doi.org/10.7812/TPP/23.022
Shah, P., et al. (2023). Unraveling the ethical enigma: Artificial intelligence in healthcare. Journal of Medical Internet Research, 25(8), e10492220. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10492220/
Sun, Q., Xie, J., Ye, N., Gu, Q., & Guo, S. (2024). Enhancing nursing and elderly care with large language models: An AI-driven framework. Proceedings of the 31st International Conference on Computational Linguistics, 10083-10094. https://aclanthology.org/2025.coling-main.673
Taipei Medical University Healthcare (TMU Healthcare). (2024, December 3). Leading and living with AI, TMU Healthcare’s pioneering smart long-term care facility. Office of Global Engagement, TMU. https://oge.tmu.edu.tw/leading-and-living-with-ai-tmu-healthcares-pinoeering-smart-long-term-care-facility/
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. https://doi.org/10.1038/s41591-018-0300-7
Weiner, E. B., Dankwa-Mullan, I., Nelson, W. A., & Hassanpour, S. (2025). Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice. PLOS digital health, 4(4), e0000810. https://doi.org/10.1371/journal.pdig.0000810
Wu, T.-Y., Majeed, A., & Kuo, K. N. (2010). An overview of the healthcare system in Taiwan. London Journal of Primary Care, 3(2), 115–119. https://doi.org/10.1080/17571472.2010.11493315
Xiao, H.-Y., & Wang, M.-Y. (2024). Progress, challenges, and development directions of long-term care in Taiwan. Atlantis Press Proceedings. https://www.atlantis-press.com/article/126008801.pdf
Yang, Y., & Lin, G. T. R. (2024). Analyzing the Shortcomings in Smart Healthcare for Remote Home Care—A Case Study of the Taiwan Market. International Journal of Environmental Research and Public Health, 21(7), 838. https://doi.org/10.3390/ijerph21070838
Yeh, C. F., Cheng, H. T., Wei, A., Chen, H. M., Kuo, P. C., Liu, K. C., Ko, M. C., Chen, R. J., Lee, P. C., Chuang, J. H., Chen, C. M., Chen, Y. C., Lee, W. J., Chien, N., Chen, J. Y., Huang, Y. S., Chang, Y. C., Huang, Y. C., Chou, N. K., Chao, K. H., Tu, Y. C., Chang, Y. C., & Liu, T. L. (2020). A cascaded learning strategy for robust COVID-19 pneumonia chest X-ray screening. arXiv preprint arXiv:2004.12786. https://arxiv.org/abs/2004.12786
Zhang, F., Bazarevsky, V., Vakunov, A., Tkachenka, A., Sung, G., Chang, C.-L., & Grundmann, M. (2020). MediaPipe Hands: On-device real-time hand tracking (arXiv:2006.10214). arXiv. https://arxiv.org/abs/2006.10214
