Publications by Year: 2021

2021
P. Arens, et al., “Real-time gait metric estimation for everyday gait training with wearable devices in people poststroke,” Wearable Technologies, vol. 2, no. e2, 2021. PDF
R. Sloutsky, et al., “Targeting post-stroke walking automaticity with a propulsion-augmenting soft robotic exosuit: toward a biomechanical and neurophysiological approach to assistance prescription,” in International IEEE/EMBS Conference on Neural Engineering, 2021. PDF
I. Wamala, et al., “Importance of Preserved Tricuspid Valve Function for Effective Soft Robotic Augmentation of the Right Ventricle in Cases of Elevated Pulmonary Artery Pressure,” Cardiovascular Engineering and Technology, 2021. Publisher's Version PDF
Y. M. Zhou, C. Hohimer, T. Proietti, C. O'Neill, and C. J. Walsh, “Kinematics-Based Control of an Inflatable Soft Wearable Robot for Assisting the Shoulder of Industrial Workers,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 2155-2162, 2021. Publisher's VersionAbstract
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.
PDF
T. Proietti, et al., “Sensing and Control of a Multi-Joint Soft Wearable Robot for Upper-Limb Assistance and Rehabilitation,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 2381-2388, 2021. Publisher's VersionAbstract
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.
PDF
Textile technology for soft robotic and autonomous garments
V. Sanchez, C. Walsh, and R. Wood, “Textile technology for soft robotic and autonomous garments,” Advanced Functional Materials, vol. 31, no. 6, 2021. PDF