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

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.

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