Human Intent for Dynamic, Rapid Motions in Unstructured Environments
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Moolchandani, Pooja
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Abstract
Struck-by threats are a leading cause of injury in unstructured, dangerous environments. Human operators must perform agile, dynamic responses in a direction of travel in order to escape any risk of injury from oncoming threats (threat-evasion). Smart robotics, specifically wearable robots, show great promise in augmenting human performance of evading oncoming threats by leveraging the agility of the human operators, while maintaining their safety in the environment. There is a lack of understanding on how to apply assistance through a wearable device for dynamic, nonlinear motions, such as threat-evasion. This thesis addresses a variety of techniques to model the highly-dimensional motion of threat-evasion using human intent in order to inform the development of potential assistance models. A human-subjects experiment was conducted to collect an array of sensor information of a subject performing threat-evasion in 8 directions of travel. The first aim of this work is to design, optimize, and validate an intent recognition system that predicts the start of threat-evasive movement and estimates an operator’s direction of travel. This work leads to understanding how to customize assistance for threat-evasion. Utilizing center of mass kinematics and joint-level biomechanics, the second aim of this work reduces the dimensionality of threat-evasion from the first aim by using human motion primitives. The findings demonstrate the best assistance strategy for augmenting human performance during threat-evasion.
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2021-04-26
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Thesis