Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/235774
Title: REAL-TIME HIERARCHICAL CLASSIFICATION OF MOTION INTENT FOR WEARABLE ROBOTICS
Authors: ASHWIN NARAYAN
ORCID iD:   orcid.org/0000-0002-4829-9473
Keywords: deep learning, human activity recognition, exoskeletons, human-robot interaction, motion sensing, real-time control
Issue Date: 8-Jan-2022
Citation: ASHWIN NARAYAN (2022-01-08). REAL-TIME HIERARCHICAL CLASSIFICATION OF MOTION INTENT FOR WEARABLE ROBOTICS. ScholarBank@NUS Repository.
Abstract: Robotic exoskeletons and prostheses could address many global health challenges associated with the aging population. Accurate sensing of the pilot’s motion intent is a major challenge in the control of these robotic devices; it allows the robots to deliver assistive forces at the pilot’s joints with the right timing and magnitude. This thesis presents a novel strategy for classifying locomotion modes for motion intent detection. Based on the idea that human motion can be described at multiple levels of specificity, a deep neural network based hierarchical classifier is developed that performs classification of locomotion mode labels at multiple levels of specificity simultaneously. The main contributions of this thesis are (1) a dataset of lower limb motion for developing algorithms for lower limb motion intent detection, (2) a novel deep learning based classification strategy for motion intent detection that is evaluated in real-time on an ankle exoskeleton robot and (3) high performance real-time hardware for sensing and real-time inference.
URI: https://scholarbank.nus.edu.sg/handle/10635/235774
Appears in Collections:Ph.D Theses (Open)

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