Multi-Modal Motion-Capture-Based Biometric Systems for Emergency Response and Patient Rehabilitation

Multi-Modal Motion-Capture-Based Biometric Systems for Emergency Response and Patient Rehabilitation

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: © 2019 |Pages: 25
DOI: 10.4018/978-1-5225-7525-2.ch007

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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Background Study And Literature Review

Rapid technological advancement has led to a new field of biometric security, in which emotional state of an individual can be predicted by analysing body movement patterns. Recognition of a person’s emotional state and appropriate response is the foundation of all social interactions. As computing resources become more sophisticated, it is reasonable to treat them as social agents. Organizations such as financial services, healthcare, telecommunication, e-commerce and e-education are developing new approaches to recognize and predict human behaviour to create more secure biometric systems. Automatic recognition of emotion has many potential applications including but not limited to smart home services, patient behaviour monitoring, passenger behaviour profiling and so forth (Adey, 2009; Fragopanagos & Taylor, 2005; Gavrilova & Monwar, 2012). We start with the brief overview of emotion recognition in biometrics, before proceeding onto the sensing devices, datasets and recent methodologies to detect emotion from biometric traits, focusing specifically on emotions from motion recognition.

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