Abstract:
Many exoskeletons today are primarily tested in controlled, steady-state laboratory conditions that are unrealistic representations of their real-world usage in which wal...Show MoreMetadata
Abstract:
Many exoskeletons today are primarily tested in controlled, steady-state laboratory conditions that are unrealistic representations of their real-world usage in which walking conditions (e.g., speed, slope, and stride length) change constantly. One potential solution is to detect these changing walking conditions online using Bayesian state estimation to deliver assistance that continuously adapts to the wearer’s gait. This paper investigates such an approach in silico, aiming to understand 1) which of the various Bayesian filter assumptions best match the problem, and 2) which gait parameters can be feasibly estimated with different combinations of sensors available to different exoskeleton configurations (pelvis, thigh, shank, and/or foot). Our results suggest that the assumptions of the Extended Kalman Filter are well suited to accurately estimate phase, stride frequency, stride length, and ramp inclination with a wide variety of sparse sensor configurations.
Published in: IEEE Robotics and Automation Letters ( Volume: 7, Issue: 3, July 2022)
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- IEEE Keywords
- Index Terms
- Bayesian Estimation ,
- Sensor Configuration ,
- Wearable Robots ,
- Kalman Filter ,
- Stride Length ,
- Extended Kalman Filter ,
- Walking Conditions ,
- Bayesian Filtering ,
- Stride Frequency ,
- Root Mean Square Error ,
- Covariance Matrix ,
- Quadratic Function ,
- Angle Measurements ,
- Variety Of Tasks ,
- Bayesian Framework ,
- Phase Variation ,
- Mahalanobis Distance ,
- Gait Cycle ,
- Restrictive Assumptions ,
- Phase Estimation ,
- Ensemble Kalman Filter ,
- Unscented Kalman Filter ,
- Actual Hardware ,
- Gait Phase ,
- Measurement Configuration ,
- Continuous Estimation ,
- Bernstein Polynomials ,
- Kinematic Measures ,
- Angular Measurements ,
- Process Noise
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Bayesian Estimation ,
- Sensor Configuration ,
- Wearable Robots ,
- Kalman Filter ,
- Stride Length ,
- Extended Kalman Filter ,
- Walking Conditions ,
- Bayesian Filtering ,
- Stride Frequency ,
- Root Mean Square Error ,
- Covariance Matrix ,
- Quadratic Function ,
- Angle Measurements ,
- Variety Of Tasks ,
- Bayesian Framework ,
- Phase Variation ,
- Mahalanobis Distance ,
- Gait Cycle ,
- Restrictive Assumptions ,
- Phase Estimation ,
- Ensemble Kalman Filter ,
- Unscented Kalman Filter ,
- Actual Hardware ,
- Gait Phase ,
- Measurement Configuration ,
- Continuous Estimation ,
- Bernstein Polynomials ,
- Kinematic Measures ,
- Angular Measurements ,
- Process Noise
- Author Keywords