Abstract
Physical activity recognition using wearable devices can provide valued information regarding an individual’s degree of functional ability and lifestyle. Smartphone-based physical activity recognition is a well-studied area. However, research on smartwatch-based physical activity recognition, on the other hand, is still in its infancy. Through a large-scale exploratory study, this work aims to investigate the smartwatch-based physical activity recognition domain. A detailed analysis of various feature banks and classification methods are carried out to find the optimum system settings for the best performance of any smartwatch-based physical activity recognition system for both personal and impersonal models in real life scenarios. To further validate our hypothesis for both personal and impersonal models, we tested single subject out cross validation process for smartwatch-based physical activity recognition.
Keywords
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Ahmad, M., Khan, A.M., Brown, J.A., Protasov, S., Khattak, A.M.: Gait fingerprinting-based user identification on smartphones. In: Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), in Conjunction with World Congress on Computational Intelligence (WCCI), Canada, pp. 3060–3067 (2016)
Ahmad, M., Alqarni, M.A., Khan, A., et al.: Smartwatch-based legitimate user identification for cloud-based secure services. Mob. Inf. Syst. 2018, 14 (2018). https://doi.org/10.1155/2018/5107024. Article ID 5107024
Khan, A.M., Lee, Y.K., Lee, S.Y., Kim, T.S.: A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer. IEEE Trans. Inf. Technol. Biomed. 14(5), 1166–1172 (2010)
Incel, O.D., Kose, M., Ersoy, C.: A review and taxonomy of activity recognition on mobile phones. BioNanoSci. 3, 145–171 (2013)
Khan, W.Z., Xiang, Y., Aalsalem, M.Y., Arshad, Q.: Mobile phone sensing systems: a survey. IEEE Commun. Surv. Tutor. 15, 402–427 (2013)
Bulling, A., Blanke, U., Schiele, B.: A tutorial on human activity recognition using body-worn inertial sensors. ACM J. Comput. Surv. 46(3), 33:1–33:33 (2014). Article No. 33
Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.T.: A survey of mobile phone sensing. IEEE Commun. Mag. 48, 140–150 (2010)
Shoaib, M.: Human activity recognition using heterogeneous sensors. In: Proceedings of the Adjunct Proceedings of the ACM Conference on Ubiquitous Computing, pp. 8–12 (2013)
Guiry, J.J., Van de Ven, P., Nelson, J.: Multi-sensor fusion for enhanced contextual awareness of everyday activities with ubiquitous devices. J. Sens. 14, 5687–5701 (2014)
Trost, S.G., Zheng, Y., Wong, W.K.: Machine learning for activity recognition: hip versus wrist data. Physiol. Meas. 35, 2183–2189 (2014)
Chernbumroong, S., Atkins, A.S., Yu, H.: Activity classification using a single wrist-worn accelerometer. In: Proceedings of the 5th IEEE International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA), pp. 1–6 (2011)
Da Silva, F.G., Galeazzo, E.: Accelerometer-based intelligent system for human movement recognition. In: Proceedings of the 5th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI), pp. 20–24 (2013)
Ramos-Garcia, R.L., Hoover, A.W.: A study of temporal action sequencing during consumption of a meal. In: Proceedings of the ACM International Conference on Bioinformatics, Computational Biology and Biomedical Informatics, p. 68 (2013)
Dong, Y., Scisco, J., Wilson, M., Muth, E., Hoover, A.: Detecting periods of eating during free-living by tracking wrist motion. IEEE J. Biomed. Health Inf. 18, 1253–1260 (2013)
Sen, S., Subbaraju, V., Misra, A., Balan, R., Lee, Y.: The case for smartwatch-based diet monitoring. In: Proceedings of the IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom), pp. 585–590 (2015)
Scholl, P.M., Van Laerhoven, K.: A feasibility study of wrist-worn accelerometer based detection of smoking habits. In: Proceedings of the 6th IEEE International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), pp. 886–891 (2012)
Parade, A., Chiu, M.C., Chadowitz, C., Ganesan, D., Kalogerakis, E.: Recognizing smoking gestures with inertial sensors on a wristband. In: Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services, pp. 149–161 (2014)
Varkey, J.P., Pompili, D., Walls, T.A.: Human motion recognition using a wireless sensor-based wearable system. Pers. Ubiquit. Comput. 16, 897–910 (2012)
Kim, H., Shin, J., Kim, S., Ko, Y., Lee, K., Cha, H., Hahm, S.-I., Kwon, T.: Collaborative classification for daily activity recognition with a smartwatch. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3707–3712 (2016)
Weiss, G.M., Timko, J.L., Gallagher, C.M., Yoneda, K., Schreiber, A.J.: Smartwatch-based activity recognition: a machine learning approach. In: Proceedings of the IEEE International Conference on Biomedical and Health Informatics (BHI), pp. 426–429 (2016)
Nurwanto, F., Ardiyanto, I., Wibirama, S.: Light sports exercise detection based on smartwatch and smartphone using k-nearest neighbor and dynamic time warping algorithm. In: Proceedings of the 8th IEEE International Conference on Information Technology and Electrical Engineering (ICITEE), pp. 1–5 (2016)
Al-Naffakh, N., Clarke, N., Dowland, P., Li, F.: Activity recognition using wearable computing. In: Proceedings of the IEEE 11th International Conference on Internet Technology and Secured Transactions (ICITST), pp. 189–195 (2016)
Huynh, T., Schiele, B.: Analyzing features for activity recognition. In: Proceedings of the Joint Conference on Smart Objects and Ambient Intelligence: Innovative Context-Aware Services: Usages and Technologies, pp. 159–163 (2005)
Khan, A.M., Siddiqi, H.M., Lee, S.W.: Exploratory data analysis of acceleration signals to select light-weight and accurate features for real-time activity recognition on smartphones. J. Sens. 13(10), 13099–13122 (2013)
Saputri, T.R.D., Khan, A.M., Lee, S.W.: User-independent activity recognition via three-stage GA-based feature selection. Int. J. Distrib. Sens. Netw. 2014, 15 (2014)
Khan, A.M., Lee, Y.-K., Kim, T.-S.: Accelerometer signal-based human activity recognition using augmented autoregressive model coefficients and artificial neural nets. In: Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5172–5175 (2008)
Su, X., Tong, H., Ji, P.: Activity recognition with smartphone sensors. Tsinghua Sci. Technol. Int. J. Inf. Sci. 19(3), 235–249 (2014)
Ahmad, M., Khan, A.M., Mazzara, M., Distefano, S., Ali, A., Tufail, A.: Extended sammon projection and wavelet kernel extreme learning machine for gait-based legitimate user identification. In: Proceedings of the 34th ACM/SIGAPP Symposium On Applied Computing, SAC 2019 (2019)
Al Jeroudi, Y.: Online sequential extreme learning machine algorithm based human activity recognition using inertial data. In: Proceedings of the IEEE 10th Control Conference (ASCC), pp. 1–6 (2015)
Shoaib, M., Bosch, S., Incel, O.D., Scholten, H., Havinga, P.J.M.: Complex human activity recognition using smartphone and wrist-worn motion sensors. J. Sens. 16, 1–24 (2016)
Shoaib, M., Bosch, S., Incel, O.D., Scholten, H., Havinga, P.J.M.: A survey of online activity recognition using mobile phones. J. Sens. 15(1), 2059–2085 (2015)
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Ahmad, M., Khan, A., Mazzara, M., Distefano, S. (2020). Seeking Optimum System Settings for Physical Activity Recognition on Smartwatches. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-17798-0_19
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DOI: https://doi.org/10.1007/978-3-030-17798-0_19
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