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Vol. 7, June, Issue 6

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ANALYSIS OF MACHINE LEARNING CLASSIFICATIONALGORITHM FOR IMPROVING ACCURACY OF IOTBASED HUMAN ACTIVITY REORGANIZATION SYSTEM

Abstract

Abstract:- Human activity recognition is currently a growing research topic for various reasons. The main goal is to recognizea activity person’s using different technologies such as cameras, orientation sensors, motion sensors, location sensors, andtime. Human activity recognition is having application areas such as pervasive computing, artificial intelligence, humancomputer interaction, health care, health outcomes, rehabilitation engineering, occupational science, and social sciences.There are numerous pervasive and ubiquitous computing systems where users’ activities play an important role. The humanactivity carries lots of information about the context and helps the systems to achieve context-awareness. In the rehabilitationarea, it helps with functional diagnosis to patient and assessing health outcomes accurately. Human activity recognition is animportant performance indicator of participation, quality of life and lifestyle. The thesis objective was to develop and evaluatea third-generation Wearable Mobility Monitoring System (WMMS) that uses features from inertial measurement sensors tocategorize activities and determine user changes-of-state in daily living environments. A custom suite of MATLAB® softwaretools were developed to assess the previous WMMS iteration and aid in third-generation WMMS algorithm construction andevaluation.Keywords – Features, Human activity, Recognition, Rehabilitation, Sensors, Wearable

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