Abstract
With the increase of disabled people, the functionalities of smart wheelchair as a mobility-assisted equipment are being more and more enriched and extended. However, fatigue detection for wheelchair users is still not explored widely. This paper proposes a complete system and approach to classify fatigue degree for manual wheelchair users. In our system, physiological and kinetic data are collected in terms of sEMG, ECG, and acceleration signals. The necessary features are then extracted from the signals and integrated with self-rating method to train a neuro-fuzzy classifier. Finally, four degrees of this fatigue status can be distinguished by our system; this can provide further fatigue prediction and alertness in case of musculoskeletal disorders (MSD) caused by underlying fatigue.
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References
World health statistics 2015: World Health Organization 2015. http://www.who.int/gho/publications/world_health_statistics/2015/en/
Simpson, R.C.: Smart wheelchairs: a literature review. J. Rehabil. Res. Development 42(4), 423 (2005)
Ma, R., Chablat, D., Bennis, F., Ma, L.: Human muscle fatigue model in dynamic motions. In: Lenarcic, J., Husty, M. (eds.) Latest Advances in Robot Kinematics, pp. 349–356. Springer, New York (2012)
Manero, R.B., Grewal, J., Michael, B., et al.: Wearable Embroidered Muscle Activity Sensing Device for the Human Upper Leg. arXiv preprint arXiv (2016)
Pilarski, P.M., Qi, L., Ferguson, P.M., et al.: Determining the time until muscle fatigue using temporally extended prediction learning. In: Proceedings of the 18th International Functional Electrical Stimulation Society Conference (IFESS), Donostia-San Sebastian, Spain, 7–8 June 2013
Rechy-Ramirez, E.J., Janet, E., Hu, H.: Stages for developing control systems using EMG and EEG signals: a survey, pp. 1744–8050. School of Computer Science and Electronic Engineering, University of Essex (2011)
Kaiser, M.S., Chowdhury, Z.I., Al Mamun, S., et al.: A neuro-fuzzy control system based on feature extraction of surface electromyogram signal for solar-powered wheelchair. Cogn. Comput. 1–9 (2016)
Nauck, D., Kruse, R.: NEFCLASSmdash; a neuro-fuzzy approach for the classification of data. In: Proceedings of the 1995 ACM Symposium on Applied Computing, pp. 461–465 (1995)
Postolache, O., Freire, J., Girao, P., et al.: Smart sensor architecture for vital signs and motor activity monitoring of wheelchair users. In: Proceedings of the 2012 IEEE 6th International Conference Sensing Technology (ICST), pp. 167–172 (2012)
Dryvendra, D., Ramalingam, M., Chinnavan, E., et al.: A better engineering design: low cost assistance kit for manual wheelchair users with enhanced obstacle detection. J. Eng. Technol. Sci. 47(4), 389–405 (2015)
Wallam, F., Asif, M.: Dynamic finger movement tracking and voicecommands based smart wheelchair. Int. J. Comput. Electr. Eng. 3(4), 497 (2011)
Dzemydienė, D., Bielskis, A.A., Andziulis, A., et al.: Recognition of human emotions in reasoning algorithms of wheelchair type robots. Informatica 21(4), 521–532 (2010)
Gravina, R., Fortino, G.: Automatic methods for the detection of accelerative cardiac defense response. IEEE Trans. Affect. Comput. 1949–3045 (2016)
Ji, Q., Lan, P., Looney, C.: A probabilistic framework for modeling and real-time monitoring human fatigue. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 36(5), 862–875 (2006)
Eskofier, B., Kugler, P., Melzer, D., et al.: Embedded classification of the perceived fatigue state of runners: Towards a body sensor network for assessing the fatigue state during running. In: Proceedings of the 2012 IEEE 9th International Conference Wearable and Implantable Body Sensor Networks (BSN), pp. 113–117 (2012)
Masuda, K., Masuda, T., Sadoyama, T., et al.: Changes in surface EMG parameters during static and dynamic fatiguing contractions. J. Electromyogr. Kinesiol. 9(1), 39–46 (1999)
Nagamine, K., Iwasawa, Y., Matsuo, Y., et al.: An estimation of wheelchair user’s muscle fatigue by accelerometers on smart devices. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, pp. 57–60. ACM (2015)
Al-Mulla, M.R., Sepulveda, F., Colley, M.: sEMG techniques to detect and predict localised muscle fatigue. INTECH Open Access Publisher, Osaka (2012)
Alexandros, P., Nikolaos, B.: A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 40(1), 1–12 (2010)
Yang, L., Ge, Y., Li, W., et al.: A home mobile healthcare system for wheelchair users. In: Proceedings of the 2014 IEEE 18th International Conference Computer Supported Cooperative Work in Design (CSCWD), pp. 609–614 (2014)
Hermens, H.J., Freriks, B., Disselhorst-Klug, C., et al.: Development of recommendations for SEMG sensors and sensor placement procedures. J. Electromyogr. Kinesiol. 10(5), 361–374 (2000)
Fortino, G., Giannantonio, R., Gravina, R., Kuryloski, P., Jafari, R.: Enabling effective programming and flexible management of efficient body sensor network applications. IEEE Trans. Hum. Mach. Syst. 43(1), 115–133 (2013)
Fortino, G., Galzarano, S., Gravina, R., Li, W.: A framework for collaborative computing and multi-sensor data fusion in body sensor networks. Inf. Fus. J. 22, 50–70 (2015)
De Luca, C.J., Gilmore, L.D., Kuznetsov, M., et al.: Filtering the surface EMG signal: movement artifact and baseline noise contamination. J. Biomech. 43(8), 1573–1579 (2010)
Andreoli, A., Gravina, R., Giannantonio, R., Pierleoni, P., Fortino, G.: SPINE-HRV: a BSN-based toolkit for heart rate variability analysis in the time-domain. In: Lay-Ekuakille, A., Mukhopadhyay, S.C. (eds.) Wearable and Autonomous Biomedical Devices and Systems for Smart Environment. LNEE, vol. 75, pp. 369–389. Springer, Heidelberg (2010)
Covello, R., Fortino, G., Gravina, R., et al.: Novel method and real-time system for detecting the Cardiac Defense Response based on the ECG. In: 2013 IEEE International Symposium on Medical Measurements and Applications Proceedings (MeMeA), pp. 53–57 (2013)
González-Izal, M., Malanda, A., Gorostiaga, E., et al.: Electromyographic models to assess muscle fatigue. J. Electromyogr. Kinesiol. 22(4), 501–512 (2012)
Tran, Y., Wijesuriya, N., Tarvainen, M., et al.: The relationship between spectral changes in heart rate variability and fatigue. J. Psychophysiol. 23(3), 143–151 (2009)
Tarvainen, M.P., Niskanen, J.P., Lipponen, J.A., et al.: Kubios HRV–heart rate variability analysis software. Comput. Meth. Program. Biomed. 113(1), 210–220 (2014)
Acknowledgement
The research is financially supported by China-Italy S&T Cooperation project “Smart Personal Mobility Systems for Human Disabilities in Future Smart Cities” (China-side Project ID: 2015DFG12210, Italy-side Project ID: CN13MO7). And also Wuhan University of Technology Graduate Student Innovation Research Project (Project ID: 2015-JL-016).
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Hu, X., Gravina, R., Li, W., Fortino, G. (2016). A Neuro-Fuzzy System for Classifying Fatigue Degree of Wheelchair User. In: Li, W., et al. Internet and Distributed Computing Systems. IDCS 2016. Lecture Notes in Computer Science(), vol 9864. Springer, Cham. https://doi.org/10.1007/978-3-319-45940-0_3
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DOI: https://doi.org/10.1007/978-3-319-45940-0_3
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