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.
In the field of wearable robotics, there has been increased interest in the creation of soft wearable robots to provide assistance and rehabilitation for those with physical impairments. Compared to traditional robots, these devices have the potential to be fully portable and lightweight, a flexibility that may allow for increased utilization time as well as enable use outside of a clinical environment. In this letter, we present a textile-based multi-joint soft wearable robot to assist the upper limb, in particular shoulder elevation and elbow extension. Before developing a portable fluidic supply system, we leverage an off-board actuation system for power and control, with the worn components weighting less than half kilogram. We showed that this robot can be mechanically transparent when powered off, not restricting users from performing movements associated with activities of daily living. Three IMUs were placed on the torso, upper arm and forearm to measure the shoulder and elbow kinematics. We found an average RMSE of ∼5 degrees when compared to an optical motion capture system. We implemented dynamic Gravity Compensation (GC) and Joint Trajectory Tracking (JTT) controllers that actively modulated actuator pressure in response to IMU readings. The controller performances were evaluated in a study with eight healthy individuals. Using the GC controller, subject shoulder muscle activity decreased with increasing magnitude of assistance and for the JTT controller, we obtained low tracking errors (mean ∼6 degrees RMSE). Future work will evaluate the potential of the robot to assist with activities in post-stroke rehabilitation.