Machine Learning Modelling Based on Smartphone Sensor Data of Human Activity Recognition

Rashid Husain*, Rabia Khan**, Rajesh Kumar Tyagi***
*-** Department of Computer Science, Sule Lamido University Kafin Hausa, Jigawa State, Nigeria.
*** Department of Computer Science and Engineering, ASET, Amity University Haryana, Manesar, India.
Periodicity:January - March'2023
DOI : https://doi.org/10.26634/jcom.10.4.19341

Abstract

Smartphone sensors produce high-dimensional feature vectors that can be utilized to recognize different human activities. However, the contribution of each vector in the identification process is different, and several strategies have been examined over time to develop a procedure that yields favorable results. This paper presents the latest Machine Learning algorithms proposed for human activity classification, which include data acquisition, data preprocessing, data segmentation, feature selection, and dataset classification into training and testing sets. The solutions are compared and thoroughly analyzed by highlighting the respective advantages and disadvantages. The results show that the Support Vector Machine (SVM) algorithm achieved an accuracy rate of 95%.

Keywords

Human Activity Recognition (HAR), Feature Selection, Machine Learning, SVM, Sensor, Accelerometer, Gyroscope.

How to Cite this Article?

Husain, R., Khan, R., and Tyagi, R. K. (2023). Machine Learning Modelling Based on Smartphone Sensor Data of Human Activity Recognition. i-manager’s Journal on Computer Science, 10(4), 1-8. https://doi.org/10.26634/jcom.10.4.19341

References

[1]. Ahmed, N., Rafiq, J. I., & Islam, M. R. (2020). Enhanced human activity recognition based on smartphone sensor data using hybrid feature selection model. Sensors, 20(1), 317. https://doi.org/10.3390/s20010317
[2]. Berchtold, M., Günther, H., Budde, M., & Beigl, M. (2011). Scheduling for a modular activity recognition system to reduce energy consumption on smartphones. In ARCS Workshops, (pp. 8).
[3]. Botilias, G. P., Pappa, L., Karvelis, P., & Stylios, C. (2022, September). Tracking individuals' health using mobile applications and machine learning. In 2022 7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) (pp. 1-6). IEEE. https://doi.org/10.1109/SEEDA-CECNSM57760. 2022.9932927
[4]. Concone, F., Gaglio, S., Lo Re, G., & Morana, M. (2017). Smartphone data analysis for human activity recognition. In AI* IA 2017 Advances in Artificial Intelligence: XVIth International Conference of the Italian Association for Artificial Intelligence, (pp. 58-71). Springer International Publishing. https://doi.org/10.1007/978-3-319-70169-1_5
[5]. Ferrari, A., Micucci, D., Mobilio, M., & Napoletano, P. (2021). Trends in human activity recognition using smart phones. Journal of Reliable Intelligent Environments, 7(3), 189-213. https://doi.org/10.1007/s40860-021-00147-0
[6]. Kadri, N., Ellouze, A., & Ksantini, M. (2020, October). Recommendation system for human physical activities using smartphones. In 2020 2nd International Conference on Computer and Information Sciences (ICCIS) (pp. 1-4). IEEE. https://doi.org/10.1109/ICCIS49240.2020.9257671
[7]. Khan, R., Hussain, R., Joon, S., & Tyagi, R. K. (2019). A new way to resolve real time traffic control system by using machine learning approach. International Journal of Research in Advent Technology, 7(10), 1-4. https://doi.org/10.32622/ijrat.710201909
[8]. Nematallah, H., Rajan, S., & Cretu, A. M. (2019, October). Logistic model tree for human activity recognition using smartphone-based inertial sensors. In 2019 IEEE Sensors (pp. 1-4). IEEE. https://doi.org/10.1109/SENSORS43011.2019.8956951
[9]. Nithya, A. A., Ishwarya, K., Mummaneni, G., & Verma, V. (2022, October). CNN based identifying human activity using smartphone sensors. In 2022 International Conference on Edge Computing and Applications (ICECAA) (pp. 1115-1120). IEEE. https://doi.org/10.1109/ICECAA55415.2022.9936202
[10]. Qi, W., Su, H., & Aliverti, A. (2020). A smartphonebased adaptive recognition and real-time monitoring system for human activities. IEEE Transactions on Human- Machine Systems, 50(5), 414-423. https://doi.org/10.1109/THMS.2020.2984181
[11]. Rabbi, J., Fuad, M., Hasan, T., & Awal, M. (2021). Human activity analysis and recognition from smartphones using machine learning techniques. arXiv preprint arXiv:2103.16490. https://doi.org/10.48550/arXiv.2103.16490
[12]. Straczkiewicz, M., James, P., & Onnela, J. P. (2019). A systematic review of smartphone-based human activity recognition for health research. arXiv preprint arXiv: 1910.03970. https://doi.org/10.48550/arXiv.1910.03970
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