Human Physical Activities Recognition (HPAR) using Health Shimmer Wearable Accelerometer Sensor Data Sets
Yogesh K M1, Doreswamy2

1Yogesh K M, Department of Computer Science, Mangalore University, Mangalore, Karnataka, India.

2Doreswamy, Department of Computer Science, Mangalore University, Mangalore, Karnataka, India.

Manuscript received on 15 May 2019 | Revised Manuscript received on 22 May 2019 | Manuscript Published on 02 June 2019 | PP: 515-520 | Volume-8 Issue-7S2 May 2019 | Retrieval Number: G10870587S219/19©BEIESP

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Abstract: Activity recognition is one of the important attributes for well being of human health and activity recognition field. Wearable sensor data sets are effectively for recognition of postural acclimation and human body motion in the realistic environment. A physical signal with respect to time-based directed toward recognizes human activity events is projected, in the view of this work usage wearable accelerometer sensing devices were implanted taking place the human body location on the upper body Chest Sensor (CS), Left Ankle Sensor (LAS) and Right Lower Arms Sensor (RLAs). Accelerometer feature extracted based acceleration signals with respect to time, physical appearance of the accelerometer x, y, and z dimension values reported/recorded using shimmer2 wearable sensor device is recommended at the categorization of the 10 different users was performed 12 different types human activities, including vigorous and moderate activities. User ages between 24 to 29 years and human body weight (HBW) are 53 to 83 Kg=m2. Results were on view a large validity performance precision and recall were getting 95 for each human activities. The whole classifiers accuracy results for all combination of the feature set of all sensors is 99:07%. The considered work could be used to observe the human body motion on different body location of users. That can be helpful for in good physical shape, physical _t health authority and also to measure the activities of healthy and unhealthy people.

Keywords: Activity Recognition, Wearable Sensor, Physical Activity.
Scope of the Article: Computer Science and Its Applications