Controlling the interaction forces between a human and an exoskeleton is crucial for providing transparency or adjusting assistance or resistance levels. However, it is an open problem to control the interaction forces of lower-limb exoskeletons designed for unrestricted overground walking. For these types of exoskeletons, it is challenging to implement force/torque sensors at every contact between the user and the exoskeleton for direct force measurement. Moreover, it is important to compensate for the exoskeleton's whole-body gravitational and dynamical forces, especially for heavy lower-limb exoskeletons. Previous works either simplified the dynamic model by treating the legs as independent double pendulums, or they did not close the loop with interaction force feedback. The proposed whole-exoskeleton closed-loop compensation (WECC) method calculates the interaction torques during the complete gait cycle by using whole-body dynamics and joint torque measurements on a hip-knee exoskeleton. Furthermore, it uses a constrained optimization scheme to track desired interaction torques in a closed loop while considering physical and safety constraints. We evaluated the haptic transparency and dynamic interaction torque tracking of WECC control on three subjects. We also compared the performance of WECC with a controller based on a simplified dynamic model and a passive version of the exoskeleton. The WECC controller results in a consistently low absolute interaction torque error during the whole gait cycle for both zero and nonzero desired interaction torques. In contrast, the simplified controller yields poor performance in tracking desired interaction torques during the stance phase

Modern biomedical devices use sensor fusion techniques to improve the classification accuracy of motion intent of users for rehabilitation application. The design of motion classifier observes significant challenges due to the large number of channels and stringent communication latency requirement. This paper proposes an edge-computing distributed neural processor to effectively reduce the data traffic and physical wiring congestion. A special local and global networking architecture is introduced to significantly reduce traffic among multi-chips in edge computing. To optimize the design space of the features selected, a systematic design methodology is proposed. A novel mixed-signal feature extraction approach with assistance of neural network distortion recovery is also provided to significantly reduce the silicon area. A 12-channel 55nm CMOS test chip was implemented to demonstrate the proposed systematic design methodology. The measurement shows the test chip consumes only 20uW power, more than 10,000X less power than the current clinically used microprocessor and can perform edge-computing networking operation within 5ms time.

We present a novel powered knee prosthesis with an active variable transmission (AVT) that adapts motor torque and speed output based on the requirements of different ambulation activities. The AVT works in combination with a spring/damper system to allow for both active and passive operation modes. Preliminary tests performed by an able-bodied subject wearing a bypass orthosis show that the proposed knee can support walking on level ground in passive mode, as well as ambulating on stairs with a reciprocal gait pattern in active mode.

Powered ankle-foot prostheses must provide high power and torque while satisfying stringent size and weight requirements. Previous works have focused on improving the prosthesis torque/weight ratio with novel actuation systems such as series and parallel elastic actuators, clutchable leverages, and pneumatic artificial muscles. In this paper, we propose an alternative design approach to minimize the prosthesis size and weight, while improving torque generation and electrical efficiency, based on a polycentric kinematic chain. The proposed approach is implemented on a novel powered polycentric ankle prosthesis (p2Ankle). Kinematics and mechatronic design are presented together with bench-top testing. Preliminary validation in standing and walking is conducted with an able-bodied user using a bypass orthosis.

Robotic prostheses can improve walking ability in persons with transfemoral amputations by closely matching kinetics and kinematics of the intact leg during walking. However, achieving this goal requires the prosthesis to adapt to walking speed, a function that no powered transfemoral prosthesis has yet achieved. In this paper, we present, and perform initial testing on a new control framework that allows biologically accurate leg function at varying walking speeds, without the need for tuning. The proposed framework comprises two novel controllers that rely on quasi-stiffness modulation in stance phase and minimum jerk trajectory in swing phase. Preliminary testing was conducted in an able-bodied subject using a bypass adapter to walk on a robotic prosthesis at five different walking speeds (from 0.62 to 1.16 m/s). Experimental results demonstrated the ability of the proposed controller to approximate intact leg function at different walking speeds.

Recent powered (or robotic) prosthetic legs independently control different joints and time periods of the gait cycle, resulting in control parameters and switching rules that can be difficult to tune by clinicians. This challenge might be addressed by a unifying control model used by recent bipedal robots, in which virtual constraints define joint patterns as functions of a monotonic variable that continuously represents the gait cycle phase. In the first application of virtual constraints to amputee locomotion, this paper derives exact and approximate control laws for a partial feedback linearization to enforce virtual constraints on a prosthetic leg. We then encode a human-inspired invariance property called effective shape into virtual constraints for the stance period. After simulating the robustness of the partial feedback linearization to clinically meaningful conditions, we experimentally implement this control strategy on a powered transfemoral leg. We report the results of three amputee subjects walking overground and at variable cadences on a treadmill, demonstrating the clinical viability of this novel control approach.

Impedance control is a common framework for control of lower-limb prosthetic devices. This approach requires choosing many impedance controller parameters. In this paper, we show how to learn these parameters for lower-limb prostheses by observation of unimpaired human walkers. We validate our approach in simulation of a transfemoral amputee, and we demonstrate the performance of the learned parameters in a preliminary experiment with a lower-limb prosthetic device.