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.

We present an autonomous and portable hip-only soft exosuit, for augmenting human walking and running that assists hip extension by delivering peak forces of 300N to the user. Different fixed assistance profiles for walking and running were applied based on an online classification algorithm. The approach is based on the biomechanical understanding that the center of mass potential energy fluctuations during walking and running are out of phase. Specifically, we monitor the vertical acceleration with an abdomen-mounted IMU at the moment of maximum hip extension. Validation is demonstrated with six subjects on the treadmill and with eight subjects outdoors. Our results demonstrated a 99.99% accuracy on average over the fourteen participants for various speeds (0.5 - 4m/s), slopes (-10 -20%), treadmill and overground terrain, loaded (13.6 kg) and unloaded, Exo On and Exo Off conditions, and different shoe types. Results from an evaluation outdoors overground on the energetics of eight subjects demonstrated a significant reduction for running when comparing Exo On to No Exo (3.9%) and for walking and running when comparing Exo On to Exo Off (12.2% and 8.2% respectively). This study represents the first demonstration of an autonomous wearable robot reducing the energy cost of running. Significant variation in response across subjects was observed, highlighting further improvements may be possible via assistance profile individualization with human-in-the-Ioop optimization.