Knee pain is prevalent in over 20% of the population, limiting the mobility of those affected. In turn, isokinetic dynamometers and robots have been used to facilitate rehabilitation for those still capable of ambulation. However, there are at most only a few wearable robots capable of delivering isokinetic training for bedridden patients. Here, we developed a wearable robot that provides bedside isokinetic training by utilizing a variable stiffness actuator and dynamic energy regeneration. The efficacy of this device was validated in a study involving six subjects with debilitating knee injuries. During two courses of rehabilitation over a total of three weeks, the average peak torque, average torque, and average work produced by their affected knees increased significantly by 81.0%, 101.4%, and 117.6%, respectively. Furthermore, the device's energy regeneration features were found capable of extending its operating time to 198 days under normal usage, representing a 57.8% increase over the same device without regeneration. These results suggest potential methodologies for delivering isokinetic joint rehabilitation to bedridden patients in areas with limited infrastructure.

The emergence of intelligent prostheses has facilitated the life and work of disabled patients. The interaction aspect of prostheses has become a highlight research topic in the field of rehabilitation robotics. However, most of the existing prosthetic interaction methods focus on the use of myoelectricity to classify finite gestures, rather than continuous (infinite) force detection, which greatly limits the use of prosthetic scenarios. In this study, a novel optical waveguide sensor was used to collect muscle deformation information from the human arm for continuous control of the prosthetic grip force. The optical waveguide sensor was embedded with carbon fiber to limit the stretching of the waveguide, which led to the optical waveguide sensor being sensitive to bending deformation. Compared with EMGs, the accuracy of continuous grip force control based on the optical waveguide sensor is higher. The R-Square for prosthetic grip force and hand grip force were 0.867 and 0.9724 in the periodic and sustaining grip force experiments, respectively. The results suggested that the proposed method could provide a new approach to the interaction of prostheses.

As the safe and comfortable human-robot interaction draw more attention, variable stiffness joints are adopted in the rehabilitation robots for the compliant experiences. For patients with joint injury, resistance training has demonstrated its efficacy in concurrently enhancing muscle strength in the rehabilitation process. Here, we introduce a compact passive joint with variable stiffness and damping generation capability for robot-assisted resistance training. The abilities of serial variable stiffness and passive angle tracking were demonstrated by a prototype. The variable stiffness mechanism was mainly formed by 3D-printing, which only weighs 0.1kg with a wide range of stiffness (0.004-11.176Nm/rad). Moreover, the torque-angular displacement characteristics show excellent linearity at high joint stiffness. Finally, the feasibility of being integrated into the wearable rehabilitation robot has been evaluated.

Wearable robotic devices often need electrical energy. An interesting idea is to collect mechanical energy during walking and convert it into electrical energy to recharge these devices directly. In this article, we built a light-weight robotic prosthesis (1.3 kg) with the feature of self-charging. During stance phase, the prosthetic ankle joint with damping, is driven by human dynamics. The rotated ankle joint backdrives the motor, and the motor works as a generator according to the electromagnetic induction theory. Five subjects participated in experiments to verify the feasibility and five speeds walking were studied (0.7, 0.9, 1.1, and 1.3 m/s treadmill speeds and one self-selected outdoor walking speed). Experimental results demonstrate that the electrical regenerative energy per step is 1.53 ± 0.29 J on average. Meanwhile, an average consumed energy per step of the robotic prosthesis is 4.64 ± 0.15 J, which means 33 ± 5% energy can be returned to the active prosthesis (battery, 24 V, 2.6 Ah).