Recent developments in soft active wearable robots can be used for upper extremity injury prevention for healthy industrial workers with better comfort than rigid systems, but there has not been control strategy proposals for such use cases. In this letter, we introduce a kinematics-based controller for an inflatable soft wearable robot that provides assistance to the shoulder quickly and accurately when needed during industrial use cases. Our approach is to use a state machine to classify user intent using shoulder and trunk kinematics estimated with body-worn inertial measurement units. We recruited eight participants to perform various tasks common in the workplace and assessed the controller's intent classification accuracy and response times, by using the users' reactions to cues as their ground truth intentions. On average, we found that the kinematics controller had 99% classification accuracy, and responded 0.8 seconds after the users reacted to the cue to begin work and 0.5 seconds after the users reacted to a cue to stop the task. In addition, we implemented an EMG-based controller for comparison, with state transitions determined by EMG-based thresholds instead of kinematics. Compared to the EMG controller, the kinematics controller required similar time to detect the users' intentions to stop overhead work but an additional 0.17 seconds on average for detecting users' intentions to begin. Although slightly slower, the kinematics controller still provided support prior to users' work initiations. We also implemented an online adaptive tuning algorithm for the kinematics controller to speed up response time while ensuring accuracy during offset transitions. This research paves the way for a further study of kinematics-based controller in a mobile system in real work environments.

Kinematics-Based Control of an Inflatable Soft Wearable Robot for Assisting the Shoulder of Industrial Workers

Proietti T.;
2021-01-01

Abstract

Recent developments in soft active wearable robots can be used for upper extremity injury prevention for healthy industrial workers with better comfort than rigid systems, but there has not been control strategy proposals for such use cases. In this letter, we introduce a kinematics-based controller for an inflatable soft wearable robot that provides assistance to the shoulder quickly and accurately when needed during industrial use cases. Our approach is to use a state machine to classify user intent using shoulder and trunk kinematics estimated with body-worn inertial measurement units. We recruited eight participants to perform various tasks common in the workplace and assessed the controller's intent classification accuracy and response times, by using the users' reactions to cues as their ground truth intentions. On average, we found that the kinematics controller had 99% classification accuracy, and responded 0.8 seconds after the users reacted to the cue to begin work and 0.5 seconds after the users reacted to a cue to stop the task. In addition, we implemented an EMG-based controller for comparison, with state transitions determined by EMG-based thresholds instead of kinematics. Compared to the EMG controller, the kinematics controller required similar time to detect the users' intentions to stop overhead work but an additional 0.17 seconds on average for detecting users' intentions to begin. Although slightly slower, the kinematics controller still provided support prior to users' work initiations. We also implemented an online adaptive tuning algorithm for the kinematics controller to speed up response time while ensuring accuracy during offset transitions. This research paves the way for a further study of kinematics-based controller in a mobile system in real work environments.
2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11382/552177
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