Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/135182
Title: A HETEROGENEOUS SENSOR SUITE FOR REDUCING THE COGNITIVE BURDEN OF UPPER LIMB ROBOTIC CONTROL
Authors: MARCUS JIAN LI GARDNER
Keywords: human machine Interface, hand prosthesis, grasp intention, computer vision, inertial measurement, mechanomyography
Issue Date: 28-Feb-2017
Source: MARCUS JIAN LI GARDNER (2017-02-28). A HETEROGENEOUS SENSOR SUITE FOR REDUCING THE COGNITIVE BURDEN OF UPPER LIMB ROBOTIC CONTROL. ScholarBank@NUS Repository.
Abstract: This thesis investigates a novel design concept for a heterogeneous sensor suite, fusing mechanomyogram sensors for muscle activation, computer vision for object recognition, and inertial measurement sensors for predicting grasp intention; using natural arm movement during reach to grasp multiple objects with various grasp patterns. Motion features are used to predict grasp intention, yielding an average classification accuracy of 100%, 82.5% and 88.9% for bottle, lid and box objects across all subjects. The novel heterogeneous sensor suite is applied to automate the grasp control of a myoelectric hand prosthesis. Real-time task-based experiments evaluated system performance by comparing it against conventional control using MMG sensors, yielding an 8.5% average faster completion time, as well as a reduction in overall cognitive and physical burden. The results of this research provide excellent potential for the use of natural motion to replace discrete muscle input as a selection and intention prediction tool.
URI: http://scholarbank.nus.edu.sg/handle/10635/135182
Appears in Collections:Ph.D Theses (Open)

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