Data-efficient human walking speed intent identification
Taylor M. Higgins, Kaitlyn J. Bresingham, James P. Schmiedeler, Patrick M. Wensing
TL;DR
This paper presents a data-efficient algorithm for identifying a person's walking speed intent in real time using minimal training data.
Contribution
A novel real-time walking speed intent identification algorithm using Mahalanobis distance with minimal training data.
Findings
The algorithm detects walking speed changes within one gait cycle with up to 87% accuracy.
Speed increases are more easily detected than speed decreases.
Accuracy improves with the magnitude of the speed change.
Abstract
The ability to accurately identify human gait intent is a challenge relevant to the success of many applications in robotics, including, but not limited to, assistive devices. Most existing intent identification approaches, however, are either sensor-specific or use a pattern-recognition approach that requires large amounts of training data. This paper introduces a real-time walking speed intent identification algorithm based on the Mahalanobis distance that requires minimal training data. This data efficiency is enabled by making the simplifying assumption that each time step of walking data is independent of all other time steps. The accuracy of the algorithm was analyzed through human-subject experiments that were conducted using controlled walking speed changes on a treadmill. Experimental results confirm that the model used for intent identification converges quickly (within 5 min…
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Taxonomy
TopicsMuscle activation and electromyography studies · Prosthetics and Rehabilitation Robotics · Balance, Gait, and Falls Prevention