Publications by Year: 2021

Mechanical stimulation (mechanotherapy) can promote skeletal muscle repair, but a lack of reproducible protocols and mechanistic understanding of the relation between mechanical cues and tissue regeneration limit progress in this field. To address these gaps, we developed a robotic device equipped with real-time force control and compatible with ultrasound imaging for tissue strain analysis. We investigated the hypothesis that specific mechanical loading improves tissue repair by modulating inflammatory responses that regulate skeletal muscle regeneration. We report that cyclic compressive loading within a specific range of forces substantially improves functional recovery of severely injured muscle in mice. This improvement is attributable in part to rapid clearance of neutrophil populations and neutrophil-mediated factors, which otherwise may impede myogenesis. Insights from this work will help advance therapeutic strategies for tissue regeneration broadly.

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.