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Multi-Modal Motion-Capture-Based Biometric Systems for Emergency Response and Patient Rehabilitation
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Author(s): Marina L. Gavrilova (University of Calgary, Canada), Ferdous Ahmed (University of Calgary, Canada), A. S. M. Hossain Bari (University of Calgary, Canada), Ruixuan Liu (University of Calgary, Canada), Tiantian Liu (University of Calgary, Canada), Yann Maret (University of Calgary, Canada), Brandon Kawah Sieu (University of Calgary, Canada)and Tanuja Sudhakar (University of Calgary, Canada)
Copyright: 2021
Pages: 26
Source title:
Research Anthology on Rehabilitation Practices and Therapy
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-3432-8.ch032
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Abstract
This chapter outlines the current state of the art of Kinect sensor gait and activity authentication. It also focuses on emotional cues that could be observed from human body and posture. It presents a prototype of a system that combines recently developed behavioral gait and posture recognition methods for human emotion identification. A backbone of the system is Kinect sensor gait recognition, which explores the relationship between joint-relative angles and joint-relative distances through machine learning. The chapter then introduces a real-time gesture recognition system developed using Kinect sensor and trained with SVM classifier. Preliminary experimental results demonstrate accuracy and feasibility of using such systems in real-world scenarios. While gait and emotion from body movement has been researched in the context of standalone biometric security systems, they were never previously explored for physiotherapy rehabilitation and real-time patient feedback. The survey of recent progress and open problems in crucial areas of medical patient rehabilitation and rescue operations conclude this chapter.
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