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.
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.
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.