The Fugl Meyer Assessment (FMA) is a widely-used assessment for tracking motor function recovery post-stroke. Due to the limited access to rehabilitation, there exists a need for remote and automated assessment solutions. Wearable sensors and data-driven methods have shown promise for enabling automatic upper extremity FMA (FMA-UE) estimation, but minimizing user input motion and aligning with current clinical activities will aid the adoption of sensor-based assessments. In this work, we present an FMA-UE estimator which can make score predictions for a key subset of the assessment (70$\% $ of all items) using data from inertial measurement units (IMUs) placed on the arms and the trunk from three volitional reaching motions representative of functional daily activities. We collected a dataset of eleven stroke participants performing a subset of FMA-UE, and three reaching motions. The FMA-UE of each participant was assessed by an occupational therapist providing the labeled score for the training data. The estimator was trained on windowed data during FMA-UE motions and was able to make score estimates from reaching motions. Through leave-one-subject-out cross validation, the estimator achieved a normalized RMSE of 7$\% $, which is comparable to or below the established minimal clinically important difference and minimal detectable change of FMA-UE of post-stroke individuals. Comparison experiments of various model designs also revealed the importance of trunk-based features inspired by compensation strategies common post stroke and features extracted from the hand sensor. The proposed estimator has the potential to broaden the possibility of automatic assessment via wearable sensors.

This paper presents a fully-integrated soft robotic glove with multi-articular textile actuators, custom soft sensors, and an intuitive state machine intent detection controller. We demonstrate that the pressurized actuators can generate motion and force comparable to natural human fingers through bench-top testing. We apply textile-elastomer capacitive sensors to the glove to track finger flexion via strain and detect contact with objects via force. Intuitive user control is achieved via a state machine controller based on signals from the integrated sensors to detect relative changes in hand-object interactions. Results from an initial evaluation with 3 participants with spinal cord injury (SCI), of varied injury levels and years since injury, wearing and controlling the glove show an average of 87% improvement in grasping force, and improvements in functional assessments for participants with recent injuries. A significant variation in response suggests further investigation is required to understand the adaptation needed across different injury levels and durations since injury. Additionally, we evaluate the controller and find an average of 3 seconds from user initiations to completed grasps, and 10% inadvertent grasp triggers and no false releases when objects are held.

In this paper, we present a design and control approach of a modular off-board actuation system with mono-articular hip exosuit, enabling the instantaneous assistive profile modification of magnitude, shape, and timing. The off-board system consists an actuation unit with two degrees of freedom that can transmit forces to our soft exosuit via Bowden cables. To perform accurate force tracking and cable slack management, we implemented a switching admittance-position control approach that utilizes the advantages of both admittance and position control. In particular, feedforward models (FFMs) are added to the admittance control to compensate variability of individual kinematics, nonlinear hip exosuit stiffness and actuator transmission loss to enable a more accurate force tracking. This system allows us to track force profiles accurately and robustly under normal walking or jogging speed. The performance of force tracking was evaluated on three subjects walking on a treadmill at 1.25 m/s with various magnitude, shape, and timings of assistive profiles and jogging on a treadmill at 2.3 m/s with varied magnitude of a representative assistance profile. Results showed that assistance could be delivered reliably across different profiles. In case of walking experiments, the errors of the magnitude, RMS-E, and timing were within 2.0 N, 8.4 N, and 1.8% when varying the magnitude, shape, and timing respectively. In case of jogging experiments, the RMS-E was within 3.0 N. This system will be used for future research examining how to individualize assistive profiles for different wearers during walking and jogging.