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2017, vol. 72, iss. 1, pp. 82-87
Classification of physical exercises using a triaxial accelerometer in a smartphone and an artificial neural network
University of Belgrade, Electrical Engineering Institute 'Nikola Tesla'
The prevalence of smartphones and their adequate computer skills can be used for detecting everyday physical exercises. Acquired information on performed exercises can be used in the field of Health Informatics. For identification of particular physical activity a number of sensors and their repositioning during exercises are needed. This paper presents a way to classify the type of exercise using only triaxial built-in accelerometric sensor in the smartphone. The smartphone itself is free to move inside the subject pocket. The problem of using a number of sensors and their repositioning during exercise is solved by raw signal filtering and by defining a set of signal descriptors. Nine characteristic exercises have been analyzed for different programs and levels of exercise. To filter the raw accelerometer signal a low-pass 10-th order Butterworth filter is used. The filtered signals are described in terms of five descriptors which are used to train an artificial neural network (ANN). Classification of the type of exercise is performed using ANN with an error of 0.7%. Some exercises can be performed with only left or right leg. The classification accuracy of proposed approach is tested in a way that the smartphone was always in the subject's right pocket even when the exercise is performed using left leg only.
Ar, I., Akgul, Y.S. (2014) A Computerized Recognition System for the Home-Based Physiotherapy Exercises Using an RGBD Camera. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(6): 1160-1171
Bonnet, V., Joukov, V., Kulic, D., Fraisse, P., Ramdani, N., Venture, G. (2016) Monitoring of Hip and Knee Joint Angles Using a Single Inertial Measurement Unit During Lower Limb Rehabilitation. IEEE Sensors Journal, 16(6): 1557-1564
Ermes, M., Parkka, J., Mantyjarvi, J., Korhonen, I. (2008) Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions. IEEE Transactions on Information Technology in Biomedicine, 12(1): 20-26
Guan, Q., Yin, X., Guo, X., Wang, G. (2016) A Novel Infrared Motion Sensing System for Compressive Classification of Physical Activity. IEEE Sensors Journal, 16(8): 2251-2259
Guo, S., Grindle, G.G., Authier, E.L., Cooper, R.A., Fitzgerald, S.G., Kelleher, A., Cooper, R. (2006) Development and Qualitative Assessment of the<tex>$hbox GAME^rm Cycle$</tex>Exercise System. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(1): 83-90
Kang, W., Han, Y. (2015) SmartPDR: Smartphone-Based Pedestrian Dead Reckoning for Indoor Localization. IEEE Sensors Journal, 15(5): 2906-2916
Li, G., Liu, T., Yi, J., Wang, H., Li, J., Inoue, Y. (2016) The Lower Limbs Kinematics Analysis by Wearable Sensor Shoes. IEEE Sensors Journal, 16(8): 2627-2638
Liao, J., Wang, Z., Wan, L., Cao, Q., Qi, H. (2014) Smart Diary: A Smartphone-based Framework for Sensing, Inferring and Logging Users’ Daily Life. IEEE Sensors Journal, str. 1-1
Maamar, H. R., Boukerche, A., Petriu, E. M. (2012) 3-D Streaming Supplying Partner Protocols for Mobile Collaborative Exergaming for Health. IEEE Transactions on Information Technology in Biomedicine, 16(6): 1079-1095
Phan, N., Ebrahimi, J., Kil, D., Piniewski, B., Dou, D. (2016) Topic-Aware Physical Activity Propagation in a Health Social Network. IEEE Intelligent Systems, 31(1): 5-14
Sardini, E., Serpelloni, M., Pasqui, V. (2015) Wireless Wearable T-Shirt for Posture Monitoring During Rehabilitation Exercises. IEEE Transactions on Instrumentation and Measurement, 64(2): 439-448
Wang, A., Chen, G., Yang, J., Zhao, S., Chang, C. (2016) A Comparative Study on Human Activity Recognition Using Inertial Sensors in a Smartphone. IEEE Sensors Journal, 16(11): 4566-4578
Wu, M., Chen, C., Wen, C., Hsu, J. (2013) Design of Pervasive Rehabilitation Monitoring for Chronic Obstructive Pulmonary Disease. IEEE Sensors Journal, 13(11): 4413-4422
Xu, J.Y., Wang, Y., Barrett, M., Dobkin, B., Pottie, G.J., Kaiser, W.J. (2016) Personalized Multilayer Daily Life Profiling Through Context Enabled Activity Classification and Motion Reconstruction: An Integrated System Approach. IEEE Journal of Biomedical and Health Informatics, 20(1): 177-188
Zelun, Z., Poslad, S. (2014) Improved Use of Foot Force Sensors and Mobile Phone GPS for Mobility Activity Recognition. IEEE Sensors Journal, 14(12): 4340-4347
Zheng, Y., Ding, X., Poon, C.C.Y., Lo, B.P.L., Zhang, H., Zhou, X., Yang, G., Zhao, N., Zhang, Y. (2014) Unobtrusive Sensing and Wearable Devices for Health Informatics. IEEE Transactions on Biomedical Engineering, 61(5): 1538-1554


article language: Serbian
document type: Professional Paper
DOI: 10.5937/tehnika1701082C
published in SCIndeks: 21/05/2017
Creative Commons License 4.0

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