Human Activity Recognition Using Smartphones Sensors for Ambient Assisted Living
DOI:
https://doi.org/10.26438/ijcse/v9i2.511Keywords:
Human Activity Recognition, Ambient Assisted Living Smartphone, ReliefF, Sequential Forward Floating SelectionAbstract
With the rapid growth in the elderly population, conventional health care system is no longer sufficient to provide personalized healthcare services for the elderly and healthcare givers are looking for a technological based solution. Ambient Assisted Living(AAL) is such a solution and at the heart of AAL is human activity recognition. Modern smartphone embedded with a lot of sensors has become an integral part of our life and is a vital option for collecting data for activity recognition. In this paper we looked at the use of smartphone accelerometer with supervised machine learning algorithm in WEKA framework for monitoring Activity of Daily Living (ADL): standing, walking, lying, walking upstairs and walking down stairs. Sitting, for the elderly in their environment of choice. We examined two common classification algorithms: Random Forest (RF), instance-based learning (KNN), RF gave us the highest accuracy of 94.4% which is considered adequate for activity recognition.
References
[1] R. Al?Shaqi, M. Mourshed. & Y. Rezgui. “Progress in ambient assisted systems for independent living by the elderly”. Springer Plus, 5:624, 1-20, DOI 10.1186/s40064-016-2272-8, 2016.
[2] M. Al-khafajiy, T. Baker, C. Chalmers, M. Asim, H. Kolivand, M. Fahim, & A. Waraich, “Remote health monitoring of elderly through wearable Sensors”. Multimedia Tools and Applications, https://doi.org/10.1007/s11042-018-7134-7, 2019.
[3] M. J. Rodrigues, O. Postolache, & F. Cercas, Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review”. Sensors, 20, 2186; doi:10.3390/s20082186, 2020.
[4] M. Z. Uddin, W. Khaksar, & J. Torresen, “Ambient Sensors for Elderly Care and Independent Living: A Survey”. Sensors, 18, 2027, 2018.
[5] R. DamaševiIius, M. Vasiljevas, J. ŠalkeviIius, & M. Wofniak, “Human Activity Recognition in AAL Environments Using Random Projections”. Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine, 4073584, doi./10.1155/2016/4073584, 2016.
[6] S. Majumder, & M. J. Deen, ”Review Smartphone Sensors for Health Monitoring and Diagnosis”; Sensors 2019, 19, 2164; doi:10.3390/s19092164.
[7] L. Parra, S. Sendra, J. M. Jiménez, & J. Lloret, “Multimedia Sensors Embedded in Smartphones for Ambient Assisted Living and e-Health”. DOI: 10.1007/s11042-015-2745-8, 2015.
[8] A. Grguric , M. Mošmondor, D. Huljenic´, “The SmartHabits: An Intelligent Privacy-Aware Home Care Assistance System”, 20.
[9] U. ZIA, W. kahalil, S. Khan, I. Ahmad, M. N. Khan, “Towards human activity recognition for ubiquitous health care using data from a waist-mounted smartphone”. Turk J Elec Eng & Comp Sci , 28, 646 – 663, doi:10.3906/elk-1901-31, 2020.
[10] S. Eisa, A. Moreira, ”A Behaviour Monitoring System (BMS) for Ambient Assisted Living”. Sensors, 17, 1946.
[11] M. Ehatisham-ul-Haq, M. A. Azam, U. Naeem, S. R?hman, A. Khalid, “Identifying Smartphone Users based on their Activity Patterns via Mobile Sensing”. Procedia Computer Science 113 (2017) 202–209, 2017.
[12] C. Wang, S. Lee, H. Ho, Y. Na, S. D. Min, “Detection of Optimal Activity Recognition Algorithm for Elderly Using Smartphone”. Advances in Computer Science and Ubiquitous Computing, DOI 10.1007/978-981-10-3023-9_157, 2017.
[13] A. Rasekh, C. A. Chen, Y. Lu “Human activity recognition using smartphone”. arXiv preprint arXiv:14018212, 2014.
[14] D. Dua, C. Graff, “UCI Machine Learning Repository”. Irvine, CA: University of California, School of Information and Computer Science, 2019.
[15] R. Khusainov, D. Azzi, I. E. Achumba, S. D. Bersch, “Real-Time Human Ambulation, Activity, and Physiological Monitoring: Taxonomy of Issues, Techniques, Applications, Challenges and Limitations”. Sensors, 13, 12852-12902; doi:10.3390/s131012852, 2013.
[16] P. Pudil, J. Novovic?ová, J. Kittler, “ Floating search methods in feature selection”. Pattern Recogn. Lett., 15(11), 1119–1125, 1994.
[17] I. Bisio, F. Lavagetto, M. Marchese, A. Sciarrone, “Smartphone-Centric Ambient Assisted Living Platform for Patients Suffering from Co-Morbidities Monitoring”. IEEE Communications Magazine 34-40, 2015.
[18] Lu, H.; Pan, W.; Lane, N.D.; Choudhury, T.; Campbell, A.T. “SoundSense: Scalable sound sensing for people-centric applications on mobile phones”. In Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services, Kraków, Poland, 22–25 June 2009.
[19] Majumder, S., Mondal, T., & Deen, MJ. “Wearable Sensors for Remote Health Monitoring”. Sensors, 17, 130; doi:10.3390/s17010130, 2017.
[20] Shoaib, M., Bosch, S., Incel, OD., Scholten, H., Havinga, PJ. “Complex human activity recognition using smartphone and wrist-worn motion sensors”. Sensors 16, 426, 2016.
[21] E. Büber, A. M. Guvensan,. “Discriminative time-domain features for activity recognition on a mobile phone.” In: 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) 2014.
[22] S. L. Lau, I. König, K. David, B. Parandian, C. Carius-Düssel, M. Schultz. “Supporting patient monitoring using activity recognition with a smartphone, in: 2010 7th International Symposium on Wireless Communication Systems (ISWCS). Presented at the 2010 7th International Symposium on Wireless Communication Systems (ISWCS),” pp. 810–814. doi:10.1109/ISWCS.2010.5624490, 2010.
[23] N. Ahmed , J.I. Rafiq and M.R Islam. “Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model.” Sensors 2020, 20, 317; doi:10.3390/s20010317
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