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http://dx.doi.org/10.22937/IJCSNS.2021.21.2.1.26

Continuous Human Activity Detection Using Multiple Smart Wearable Devices in IoT Environments  

Alshamrani, Adel (Department of Cybersecurity College of Computer Science and Engineering University of Jeddah)
Publication Information
International Journal of Computer Science & Network Security / v.21, no.2, 2021 , pp. 221-228 More about this Journal
Abstract
Recent improvements on the quality, fidelity and availability of biometric data have led to effective human physical activity detection (HPAD) in real time which adds significant value to applications such as human behavior identification, healthcare monitoring, and user authentication. Current approaches usually use machine-learning techniques for human physical activity recognition based on the data collected from wearable accelerometer sensor from a single wearable smart device on the user. However, collecting data from a single wearable smart device may not provide the complete user activity data as it is usually attached to only single part of the user's body. In addition, in case of the absence of the single sensor, then no data can be collected. Hence, in this paper, a continuous HPAD will be presented to effectively perform user activity detection with mobile service infrastructure using multiple wearable smart devices, namely smartphone and smartwatch placed in various locations on user's body for more accurate HPAD. A case study on a comprehensive dataset of classified human physical activities with our HAPD approach shows substantial improvement in HPAD accuracy.
Keywords
Wearable sensors; biometric systems; biomedical monitoring; low-cost health care;
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1 Reyes-Ortiz, J.L.; Oneto, L.; Sama, A.; Parra, X.; Anguita, D. Transition-aware human activity recognition288using smartphones. Neurocomputing2016,171, 754-767.   DOI
2 Cantoral-Ceballos, J.A.; Nurgiyatna, N.; Wright, P.; Vaughan, J.; Brown-Wilson, C.; Scully, P.J.; Ozanyan,262K.B. Intelligent carpet system, based on photonic guided-path tomography, for gait and balance monitoring263in home environments.IEEE sensors Journal2014,15, 279-289.   DOI
3 Kim, E.; Helal, S.; Cook, D. Human activity recognition and pattern discovery.IEEE Pervasive265Computing/IEEE Computer Society [and] IEEE Communications Society2010,9, 48.
4 Chen, K.; Zhang, D.; Yao, L.; Guo, B.; Yu, Z.; Liu, Y. Deep learning for sensor-based human activity267recognition: overview, challenges and opportunities.arXiv preprint arXiv:2001.074162020.
5 Han, M.; Lee, Y.K.; Lee, S.; others. Comprehensive context recognizer based on multimodal sensors in a269smartphone.Sensors2012,12, 12588-12605.   DOI
6 Ravi, N.; Dandekar, N.; Mysore, P.; Littman, M.L. Activity recognition from accelerometer data. Aaai, 2005,273Vol. 5, pp. 1541-1546.
7 Mahoney, J.M.; Rhudy, M.B. Methodology and validation for identifying gait type using machine learning279on IMU data.Journal of medical engineering & technology2019,43, 25-32.   DOI
8 Lester, J.; Choudhury, T.; Borriello, G. A practical approach to recognizing physical activities. International conference on pervasive computing. Springer, 2006, pp. 1-16.
9 Shoaib, M.; Scholten, H.; Havinga, P.J. Towards physical activity recognition using smartphone sensors.2852013 IEEE 10th international conference on ubiquitous intelligence and computing and 2013 IEEE 10th286international conference on autonomic and trusted computing. IEEE, 2013, pp. 80-87.
10 Ehatisham-Ul-Haq, M.; Javed, A.; Azam, M.A.; Malik, H.M.; Irtaza, A.; Lee, I.H.; Mahmood, M.T. Robust292human activity recognition using multimodal feature-level fusion.IEEE Access2019,7, 60736-60751.   DOI
11 Holien, K. Gait recognition under non-standard circumstances. Master's thesis, 2008.
12 Reddy, S.; Mun, M.; Burke, J.; Estrin, D.; Hansen, M.; Srivastava, M. Using mobile phones to determine283transportation modes.ACM Transactions on Sensor Networks (TOSN)2010,6, 13.
13 .Makihara, Y.; Matovski, D.S.; Nixon, M.S.; Carter, J.N.; Yagi, Y. Gait recognition: Databases, representations,295and applications.Wiley Encyclopedia of Electrical and Electronics Engineering2015, pp. 1-15.
14 Das, S.; Green, L.; Perez, B.; Murphy, M. Detecting User Activities using the Accelerometer on Android297Smartphones (2010).
15 Sprager, S.; Juric, M. Inertial sensor-based gait recognition: A review.Sensors2015,15, 22089-22127.   DOI
16 Kwapisz, J.R.; Weiss, G.M.; Moore, S.A. Activity recognition using cell phone accelerometers.ACM271SigKDD Explorations Newsletter2011,12, 74-82..   DOI
17 Wang, J.; Chen, R.; Sun, X.; She, M.F.; Wu, Y. Recognizing human daily activities from accelerometer signal.277Procedia Engineering2011,15, 1780-1786.   DOI
18 Qin, Z.; Zhang, Y.; Meng, S.; Qin, Z.; Choo, K.K.R. Imaging and fusing time series for wearable sensor-based290human activity recognition.Information Fusion2020,53, 80-87.   DOI
19 Lester, J.; Hannaford, B.; Borriello, G. "Are you with me?"-using accelerometers to determine if two301devices are carried by the same person. International Conference on Pervasive Computing. Springer, 2004,302pp. 33-50.
20 Brezmes, T.; Gorricho, J.L.; Cotrina, J. Activity recognition from accelerometer data on a mobile phone.275International Work-Conference on Artificial Neural Networks. Springer, 2009, pp. 796-799.
21 Le Cam, L. The central limit theorem around 1935.Statistical science1986, pp. 78-91.
22 Lara, O.D.; Labrador, M.A. A survey on human activity recognition using wearable sensors.IEEE305communications surveys & tutorials2012,15, 1192-1209.   DOI
23 Derawi, M.; Bours, P. Gait and activity recognition using commercial phones.computers & security2013,30739, 137-144.   DOI
24 Hoang, T.; Choi, D.; Vo, V.; Nguyen, A.; Nguyen, T. A lightweight gait authentication on mobile phone309regardless of installation error. IFIP International Information Security Conference. Springer, 2013, pp.31083-101
25 Cunningham, P.; Delany, S.J. k-Nearest neighbour classifiers.Multiple Classifier Systems2007,34, 1-17.