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Open AccessArticle

Subject- and Environment-Based Sensor Variability for Wearable Lower-Limb Assistive Devices

by Nili E. Krausz 1,2,*, Blair H. Hu 1,2 and Levi J. Hargrove 1,2,3
1
Neural Engineering for Prosthetics and Orthotics Lab (NEPOL), Center of Bionic Medicine, Shirley Ryan AbilityLab (formerly RIC), Chicago, IL 60611, USA
2
Biomedical Engineering Department, Northwestern University, Evanston, IL 60208, USA
3
Physical Medicine and Rehabilitation Department, Northwestern University, Evanston, IL 60208, USA
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(22), 4887; https://doi.org/10.3390/s19224887
Received: 16 September 2019 / Revised: 29 October 2019 / Accepted: 6 November 2019 / Published: 8 November 2019
(This article belongs to the Special Issue Sensor Fusion in Assistive and Rehabilitation Robotics)
Significant research effort has gone towards the development of powered lower limb prostheses that control power during gait. These devices use forward prediction based on electromyography (EMG), kinetics and kinematics to command the prosthesis which locomotion activity is desired. Unfortunately these predictions can have substantial errors, which can potentially lead to trips or falls. It is hypothesized that one reason for the significant prediction errors in the current control systems for powered lower-limb prostheses is due to the inter- and intra-subject variability of the data sources used for prediction. Environmental data, recorded from a depth sensor worn on a belt, should have less variability across trials and subjects as compared to kinetics, kinematics and EMG data, and thus its addition is proposed. The variability of each data source was analyzed, once normalized, to determine the intra-activity and intra-subject variability for each sensor modality. Then measures of separability, repeatability, clustering and overall desirability were computed. Results showed that combining Vision, EMG, IMU (inertial measurement unit), and Goniometer features yielded the best separability, repeatability, clustering and desirability across subjects and activities. This will likely be useful for future application in a forward predictor, which will incorporate Vision-based environmental data into a forward predictor for powered lower-limb prosthesis and exoskeletons. View Full-Text
Keywords: assistive robotics; prosthetics; sensor fusion; intention detection; computer vision; environmental sensing assistive robotics; prosthetics; sensor fusion; intention detection; computer vision; environmental sensing
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Krausz, N.E.; Hu, B.H.; Hargrove, L.J. Subject- and Environment-Based Sensor Variability for Wearable Lower-Limb Assistive Devices. Sensors 2019, 19, 4887.

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