All Publications

J. Kim, et al., “Reducing the energy cost of walking with low assistance levels through optimized hip flexion assistance from a soft exosuit,” Nature - Scientific Reports, vol. 12, no. 11004, 2022. Publisher's VersionAbstract
As we age, humans see natural decreases in muscle force and power which leads to a slower, less efficient gait. Improving mobility for both healthy individuals and those with muscle impairments/weakness has been a goal for exoskeleton designers for decades. In this work, we discover that significant reductions in the energy cost required for walking can be achieved with almost 50% less mechanical power compared to the state of the art. This was achieved by leveraging human-in-the-loop optimization to understand the importance of individualized assistance for hip flexion, a relatively unexplored joint motion. Specifically, we show that a tethered hip flexion exosuit can reduce the metabolic rate of walking by up to 15.2 ± 2.6%, compared to locomotion with assistance turned off (equivalent to 14.8% reduction compared to not wearing the exosuit). This large metabolic reduction was achieved with surprisingly low assistance magnitudes (average of 89 N, ~ 24% of normal hip flexion torque). Furthermore, the ratio of metabolic reduction to the positive exosuit power delivered was 1.8 times higher than ratios previously found for hip extension and ankle plantarflexion. These findings motivated the design of a lightweight (2.31 kg) and portable hip flexion assisting exosuit, that demonstrated a 7.2 ± 2.9% metabolic reduction compared to walking without the exosuit. The high ratio of metabolic reduction to exosuit power measured in this study supports previous simulation findings and provides compelling evidence that hip flexion may be an efficient joint motion to target when considering how to create practical and lightweight wearable robots to support improved mobility.
H. D. Yang, M. Cooper, A. Eckert-Erdheim, D. Orzel, and C. J. Walsh, “A Soft Exosuit Assisting Hip Abduction for Knee Adduction Moment Reduction During Walking,” IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 7439-7446, 2022. Publisher's VersionAbstract
The knee joint experiences significant torques in the frontal plane to keep the body upright during walking. Excessive loading over time can lead to knee osteoarthritis (OA), the progression of which is correlated with external knee adduction moment (KAM). In this paper, we present a wearable soft robotic exosuit that applies a hip abduction torque and evaluate its ability to reduce KAM. The exosuit uses a portable cable actuation system to generate torque when desired while remaining unrestrictive when unpowered. We explored five different force profiles on healthy participants (N=8) walking on an instrumented treadmill at 1.25 m/s. For each force profile, we tested two peak force levels: 15% and 20% of bodyweight. We observed KAM reductions with two of the five profiles. With Force Profile 2 (FP2), peak KAM was reduced by 9.61% and impulse KAM by 12.76%. With Force Profile 5 (FP5), we saw reductions of peak KAM by 6.14% and impulse KAM by 21.09%. These initial findings show that the device has the ability to change walking biomechanics in a consistent and potentially beneficial way.
S. E. Root, et al., “An Expanding Foam-Fabric Orthopedic Cast,” Advanced Materials Technologies, pp. 2101563, 2022. PDF
K. Swaminathan, et al., “Ankle resistance with a unilateral soft exosuit increases plantarflexor effort during pushoff in unimpaired individuals,” Journal of NeuroEngineering and Rehabilitation, vol. 18, no. 182, 2021. PDF
R. W. Nuckols, S. Lee, K. Swaminathan, D. Orzel, R. D. Howe, and C. J. Walsh, “Individualization of exosuit assistance based on measured muscle dynamics during versatile walking,” Science Robotics, vol. 6, no. 60, 2021. Publisher's Version
F. Porciuncula, et al., “Targeting Paretic Propulsion and Walking Speed With a Soft Robotic Exosuit: A Consideration-of-Concept Trial,” Frontiers in Neurorobotics, vol. 15, no. 689577, 2021. PDF
D. Arumukhom Revi, S. De Rossi, C. Walsh, and L. Awad, “Estimation of Walking Speed and Its Spatiotemporal Determinants Using a Single Inertial Sensor Worn on the Thigh From Healthy to Hemiparetic Walking,” Sensors, vol. 21, no. 6976, 2021. PDF
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.
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.
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
D. Arumukhom Revi, A. Alvarez, C. J. Walsh, S. De Rossi, and L. Awad, “Indirect measurement of anterior-posterior ground reaction forces using a minimal set of wearable inertial sensors: from healthy to hemiparetic walking,” Journal of NeuroEngineering and Rehailitation, vol. 17, no. 82, 2020. PDF
O. A. Araromi, et al., “Ultra-sensitive and resilient compliant strain gauges for soft machines,” Nature, vol. 587, pp. 219-224, 2020. Publisher's VersionAbstract
Soft machines are a promising design paradigm for human-centric devices and systems required to interact gently with their environment. To enable soft machines to respond intelligently to their surroundings, compliant sensory feedback mechanisms are needed. Specifically, soft alternatives to strain gauges—with high resolution at low strain (less than 5 per cent)—could unlock promising new capabilities in soft systems. However, currently available sensing mechanisms typically possess either high strain sensitivity or high mechanical resilience, but not both. The scarcity of resilient and compliant ultra-sensitive sensing mechanisms has confined their operation to laboratory settings, inhibiting their widespread deployment. Here we present a versatile and compliant transduction mechanism for high-sensitivity strain detection with high mechanical resilience, based on strain-mediated contact in anisotropically resistive structures (SCARS). The mechanism relies upon changes in Ohmic contact between stiff, micro-structured, anisotropically conductive meanders encapsulated by stretchable films. The mechanism achieves high sensitivity, with gauge factors greater than 85,000, while being adaptable for use with high-strength conductors, thus producing sensors resilient to adverse loading conditions. The sensing mechanism also exhibits high linearity, as well as insensitivity to bending and twisting deformations—features that are important for soft device applications. To demonstrate the potential impact of our technology, we construct a sensor-integrated, lightweight, textile-based arm sleeve that can recognize gestures without encumbering the hand. We demonstrate predictive tracking and classification of discrete gestures and continuous hand motions via detection of small muscle movements in the arm. The sleeve demonstration shows the potential of the SCARS technology for the development of unobtrusive, wearable biomechanical feedback systems and human–computer interfaces.
Smart Thermally Actuating Textiles
V. Sanchez, et al., “Smart Thermally Actuating Textiles,” Advanced Materials Technologies, 2020. PDF
C. Siviy, et al., “Offline Assistance Optimization of a Soft Exosuit for Augmenting Ankle Power of Stroke Survivors During Walking,” IEEE Robotics and Automation Letters, vol. 5, no. 2, 2020. PDF
R. W. Nuckols, K. Swaminathan, S. Lee, L. Awad, C. J. Walsh, and R. D. Howe, “Automated detection of soleus concentric contraction in variable gait conditions for improved exosuit control,” in IEEE International Conference on Robotics and Automation (ICRA), 2020. PDF
Y. Jin, et al., “Soft Sensing Shirt for Shoulder Kinematics Estimation,” in IEEE International Conference on Robotics and Automation (ICRA), 2020. PDF
C. Correia, et al., “Improving Grasp Function after Spinal Cord Injury with a Soft Robotic Glove,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 6, pp. 1407-1415, 2020. PDF