Pervasive motion tracking and physiological monitoring

This thesis presents a new system of monitoring human motion and muscle activity concurrently, in pervasive and uncontrolled environments, for prolonged periods of time. Current technologies such as optical based motion tracking and electromyography (EMG) are considered the gold standard, but have l...

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Bibliographic Details
Main Author: Woodward, Richard
Other Authors: Vaidyanathan, Ravi ; Shefelbine, Sandra
Published: Imperial College London 2015
Subjects:
621
Online Access:http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.676820
Description
Summary:This thesis presents a new system of monitoring human motion and muscle activity concurrently, in pervasive and uncontrolled environments, for prolonged periods of time. Current technologies such as optical based motion tracking and electromyography (EMG) are considered the gold standard, but have limited use outside of a controlled laboratory environment. Restraints on collection durations, due to temporary sensors, as well as a limited collection space in which monitoring is capable, results in a constrained system which is not suitable for prolonged observation. Using a custom made inertial measurement unit (IMU) and mechanomyography (MMG) sensor, information from both motion and muscle activity was combined, in order to better understand human activity by allowing prolonged collection in unrestricted environments. IMU and MMG measurements have been compared to standard optical tracking and EMG measurements, demonstrating the viability of this technology in a clinical setting and particularly in the natural environment. This novel sensor is lightweight, inexpensive, low power, wireless, easy to use, gives results comparable to standard laboratory techniques, and is able to monitor motion and muscle activity over long periods of time. This work shows a strong agreement with the current literature on MMG response to increments of force, and a greater sensitivity to muscular fatigue detection when compared against EMG, all through pervasive studies. Using machine learning and pattern recognition methods, gait analysis and detection of progressive change over time was achieved in typical and atypical conditions, over prolonged periods. Finally, this work has shown applicable use in prosthesis control and gesture switching. Outside of muscle monitoring, alternative uses have been established, with preliminary results showing a suitable use in foetal monitoring. This work establishes a novel method of human motion and muscle monitoring which produces a suitably high accuracy when compared against the gold standard, however, without the limitations which confine the wearer to a finite space or limited duration time. The studies presented here introduce a number of areas in which prolonged and pervasive collection can expand this field, while producing complementary results against laboratory based technology.