Abstract
Perceiving human activity has earned a great amount of interest and has brought noteworthy research in recognizing logical data helpful to human activity recognition. It is the most essential technology behind different applications including health care, survey systems, surveillance, and many more. This paper presents work on human activity detection based on data collected from smartphones inertial sensors. Smartphones have installed inbuilt sensors that are fit to detect logical data of its clients, having a wide range capability of network connections. Installed sensors which include gyroscope sensor, tri-axial linear accelerometer, tri-axial accelerometer, and direction sensors are utilized for movement information assortment. Dataset used includes six activities: sitting, standing, laying, walking, walking upstairs and walking downstairs. In this paper, support vector machine learning algorithm with Gaussian radial basis kernel to classify all the six activities uniquely is used which resulted in better performance rate and less error rate as compared to the previous works already done in the area of HAR. Overlapping of dynamic and non-dynamic activity is improved with the overall accuracy of 98% which is more than the previous work results.
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Swarnakar, S.K., Agrawal, H., Goel, A. (2021). Smartphone Inertial Sensors-Based Human Activity Detection Using Support Vector Machine. In: Sharma, T.K., Ahn, C.W., Verma, O.P., Panigrahi, B.K. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1381. Springer, Singapore. https://doi.org/10.1007/978-981-16-1696-9_22
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