Development of An Integrated Framework for Cyber Physical System (CPS)-Enabled Rehabilitation System
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Published
Aug 24, 2021
Jianshe Feng
Feng Zhu
Pin Li
Hossein Davari
Jay Lee
Abstract
A Cyber-Physical System (CPS)-enabled rehabilitation system framework for enhanced recovery rate in gait training systems is presented in this paper. Recent advancements in sensing and data analytics have paved the way for the transformation of healthcare systems from experience-based to evidence-based. To this end, this paper introduces a CPS-enabled rehabilitation system that collects, processes, and models the data from patient and rehabilitative training machines. This proposed system consists of a set of sensors to collect various physiological data as well as machine parameters. The sensors and data acquisition systems are connected to an edge computing unit that handles the data preprocessing, analytics, and results visualization. Advanced machine learning algorithms are used to analyze data from physiological data, machine parameters, and patients’ metadata to quantify each patient’s recovery progress, devise personalized treatment strategies, adjust machine parameters for optimized performance, and provide feedback regarding patient’s adherence to instructions. Moreover, the accumulation of the knowledge gathered by patients with different conditions can provide a powerful tool for better understanding the human-machine interaction and its impact on patient recovery. Such system can eventually serve as a ‘Virtual Doctor’, providing accurate feedback and personalized treatment strategies for patients.
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Keywords
Cyber, Rehabilitation, PHM, Human, Health
References
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Chang, W., & Kim, Y. (2013). Robot-assisted therapy in stroke rehabilitation. journal of Stroke(15(3)), 174. doi:10.5853/jos.2013.15.3.174
Djurdjanovic, D., Lee, J., & Ni, J. (2003). Watchdog Agent—an infotronics-based prognostics approach for product performance degradation assessment and prediction. Advanced Engineering Informatics, 17(3-4), 109-125. doi:10.1016/j.aei.2004.07.005
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Feng, J. (2020). Methodology of Adaptive Prognostics and Health Management in Dynamic Work Environment. University of Cincinnati,
Feng, J., Jia, X., Cai, H., Zhu, F., Li, X., & Lee, J. (2020). Cross Trajectory Gaussian Process Regression Model for Battery Health Prediction. Journal of modern power systems and clean energy. doi:10.35833/MPCE.2019.000142
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Gao, Y., & Cui, Y. (2020). Deep transfer learning for reducing health care disparities arising from biomedical data inequality. Nature communications, 11(1), 1-8.
Hagenbuchner, M., Tsoi, A. C., & Sperduti, A. (2001). A supervised self-organizing map for structured data. In Advances in Self-Organising Maps (pp. 21-28): Springer.
Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398): John Wiley & Sons.
Iso-Ketola, P., Karinsalo, T., & Vanhala, J. (2008). HipGuard: A wearable measurement system for patients recovering from a hip operation. 2008 Second International Conference on Pervasive Computing Technologies for Healthcare. doi: 10.1109/PCTHEALTH.2008.4571068,
Jia, X., Huang, B., Feng, J., Cai, H., & Lee, J. (2018). A review of PHM Data Competitions from 2008 to 2017: Methodologies and Analytics. Proceedings of the Annual Conference of the Prognostics and Health Management Society. doi: 10.1234/PHMCONF.2018.V10I1.462,
Joe Qin, S. (2003). Statistical process monitoring: basics and beyond. Journal of Chemometrics: A Journal of the Chemometrics Society, 17(8‐9), 480-502. doi:10.1002/cem.800
Kailanto, H., Hyvarinen, E., & Hyttinen, J. (2008). Mobile ECG measurement and analysis system using mobile phone as the base station. 2008 Second International Conference on Pervasive Computing Technologies for Healthcare. doi: 10.1109/pcthealth.2008.4571014,
Konstantas, D., & Herzog, R. (2003). Continuous monitoring of vital constants for mobile users: the MobiHealth approach. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No. 03CH37439). doi: 10.1109/IEMBS.2003.1280970,
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Lee, J., Ni, J., Singh, J., Jiang, B., Azamfar, M., & Feng, J. (2020). Intelligent Maintenance Systems and Predictive Manufacturing. Journal of Manufacturing Science and Engineering, 142(11). doi:10.1115/1.4047856
Lee, J., Yang, S., Lapira, E., Kao, H., & Yen, N. (2012). Recent Advances and Trends on Cloud-based Machinery Prognostics and Health Management. PECCS 2012 - Proceedings of the 2nd International Conference on Pervasive Embedded Computing and Communication Systems. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84862120187&partnerID=tZOtx3y1,
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Mao, Y.-R., Lo, W. L., Lin, Q., Li, L., Xiao, X., Raghavan, P., & Huang, D.-F. (2015). The effect of body weight support treadmill training on gait recovery, proximal lower limb motor pattern, and balance in patients with subacute stroke. BioMed research international, 2015. doi:10.1155/2015/175719
McArthur, C., Saari, M., Heckman, G. A., Wellens, N., Weir, J., Hebert, P., . . . Hirdes, J. P. (2021). Evaluating the effect of COVID-19 pandemic lockdown on long-term care residents’ mental health: a data-driven approach in new brunswick. Journal of the American Medical Directors Association, 22(1), 187-192. doi:10.1016/j.jamda.2020.10.028
Noble, W. S. (2006). What is a support vector machine? Nature biotechnology, 24(12), 1565-1567.
Olszewski, R. T. (2001). Generalized feature extraction for structural pattern recognition in time-series data. Retrieved from https://www.cs.cmu.edu/~bobski/pubs/tr01108-twosided.pdf
Pang, Z., Chen, Q., & Zheng, L. (2009). A pervasive and preventive healthcare solution for medication noncompliance and daily monitoring. 2009 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies. doi: 10.1109/ISABEL.2009.5373681,
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117. doi:10.1016/j.neunet.2014.09.003
Shynayder, V., Chen, B.-r., Lorincz, K., Fulford-Jones, T., & Welsh, M. (2005). Sensor networks for medical care. Proc. ACM SenSys. doi: 10.1145/1098918.1098979,
Sung, M., Marci, C., & Pentland, A. (2005). Wearable feedback systems for rehabilitation. Journal of neuroengineering and rehabilitation, 2(1), 1-12. doi:10.1186/1743-0003-2-17
Taub, E., Uswatte, G., & Pidikiti, R. (1999). Constraint-induced movement therapy: a new family of techniques with broad application to physical rehabilitation-a clinical review. Journal of rehabilitation research and development, 36(3), 237-251.
Wang, C.-C., Chiang, C.-Y., Lin, P.-Y., Chou, Y.-C., Kuo, I.-T., Huang, C.-N., & Chan, C.-T. (2008). Development of a fall detecting system for the elderly residents. 2008 2nd International Conference on Bioinformatics and Biomedical Engineering. doi: 10.1109/ICBBE.2008.669,
WHO. (2003). The world health report 2003: shaping the future: World Health Organization.
WHO. (2011). World report on disability 2011: World Health Organization.
Zhang, Y., Qiu, M., Tsai, C.-W., Hassan, M. M., & Alamri, A. (2015). Health-CPS: Healthcare cyber-physical system assisted by cloud and big data. IEEE Systems Journal, 11(1), 88-95. doi:10.1109/JSYST.2015.2460747
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Technical Papers