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.

Exosuits represent a new approach for applying assistive forces to an individual, using soft textiles to interface to the wearer and transmit forces through specified load paths. In this paper we present a body-worn, multi-joint soft exosuit that assists both ankle plantar flexion and hip flexion through a multiarticular load path, and hip extension through a separate load path, at walking speeds up to 1.79m/s (4.0mph). The exosuit applies forces of 300N in the multiarticular load path and 150N in hip extension, which correspond to torques of 21% and 19% of the nominal biological moments at the ankle and hip during unloaded walking. The multi-joint soft exosuit uses a new actuation approach that exploits joint synergies, with one motor actuating the multiarticular load paths on both legs and one motor actuating the hip extension load paths on both legs, in order to reduce the total system weight. Control is accomplished by an algorithm that uses only a gyroscope at the heel and a load cell monitoring the suit tension, and is shown to adapt within a single step to changes in cadence. Additionally, the control algorithm can create slack in the suit during non-level-ground walking motions such as stepping over obstacles so that the system can be transparent to the wearer when required. The resulting system consumes 137W, and has a mass of 6.5kg including batteries.