International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
권호 표지
DOI QR코드

DOI QR Code

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)
  • Received : 2021.02.05
  • Published : 2021.02.28
Download PDF
⟨ Previous Next ⟩

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

Acknowledgement

This work was funded by the Deanship of Scientific Research (DSR), University of Jeddah, Jeddah, Saudi Arabia, under grant No. (UJ-02-102-DR). The author, therefore, acknowledges with thanks DSR, University of Jeddah, Jeddah, Saudi Arabia, technical and financial support.

References

  1. 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. https://doi.org/10.1109/JSEN.2014.2341455
  2. Kim, E.; Helal, S.; Cook, D. Human activity recognition and pattern discovery.IEEE Pervasive265Computing/IEEE Computer Society [and] IEEE Communications Society2010,9, 48.
  3. 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.
  4. Han, M.; Lee, Y.K.; Lee, S.; others. Comprehensive context recognizer based on multimodal sensors in a269smartphone.Sensors2012,12, 12588-12605. https://doi.org/10.3390/s120912588
  5. Kwapisz, J.R.; Weiss, G.M.; Moore, S.A. Activity recognition using cell phone accelerometers.ACM271SigKDD Explorations Newsletter2011,12, 74-82.. https://doi.org/10.1145/1964897.1964918
  6. Ravi, N.; Dandekar, N.; Mysore, P.; Littman, M.L. Activity recognition from accelerometer data. Aaai, 2005,273Vol. 5, pp. 1541-1546.
  7. 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.
  8. Wang, J.; Chen, R.; Sun, X.; She, M.F.; Wu, Y. Recognizing human daily activities from accelerometer signal.277Procedia Engineering2011,15, 1780-1786. https://doi.org/10.1016/j.proeng.2011.08.331
  9. 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. https://doi.org/10.1080/03091902.2019.1599073
  10. Lester, J.; Choudhury, T.; Borriello, G. A practical approach to recognizing physical activities. International conference on pervasive computing. Springer, 2006, pp. 1-16.
  11. 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.
  12. 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.
  13. Reyes-Ortiz, J.L.; Oneto, L.; Sama, A.; Parra, X.; Anguita, D. Transition-aware human activity recognition288using smartphones. Neurocomputing2016,171, 754-767. https://doi.org/10.1016/j.neucom.2015.07.085
  14. 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. https://doi.org/10.1016/j.inffus.2019.06.014
  15. 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. https://doi.org/10.1109/ACCESS.2019.2913393
  16. Holien, K. Gait recognition under non-standard circumstances. Master's thesis, 2008.
  17. .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.
  18. Das, S.; Green, L.; Perez, B.; Murphy, M. Detecting User Activities using the Accelerometer on Android297Smartphones (2010).
  19. Sprager, S.; Juric, M. Inertial sensor-based gait recognition: A review.Sensors2015,15, 22089-22127. https://doi.org/10.3390/s150922089
  20. Cunningham, P.; Delany, S.J. k-Nearest neighbour classifiers.Multiple Classifier Systems2007,34, 1-17.
  21. 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.
  22. Le Cam, L. The central limit theorem around 1935.Statistical science1986, pp. 78-91.
  23. Lara, O.D.; Labrador, M.A. A survey on human activity recognition using wearable sensors.IEEE305communications surveys & tutorials2012,15, 1192-1209. https://doi.org/10.1109/SURV.2012.110112.00192
  24. Derawi, M.; Bours, P. Gait and activity recognition using commercial phones.computers & security2013,30739, 137-144. https://doi.org/10.1016/j.cose.2013.07.004
  25. 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