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


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%.


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.


[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).
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