Identification of Hip and Knee Joint Impedance During the Swing Phase of Walking

Knowledge on joint impedance during walking in various conditions is relevant for clinical decision-making and the development of robotic gait trainers, leg prostheses, leg orthotics and wearable exoskeletons. Whereas ankle impedance during walking has been experimentally assessed, knee and hip joint impedance during walking have not been identified yet. Here we developed and evaluated a lower limb perturbator to identify hip, knee and ankle joint impedance during treadmill walking. The lower limb perturbator (LOPER) consists of an actuator connected to the thigh via rods. The LOPER allows to apply force perturbations to a free-hanging leg, while standing on the contralateral leg, with a bandwidth of up to 39 Hz. While walking in minimal impedance mode, the interaction forces between LOPER and the thigh were low (<5 N) and the effect on the walking pattern was smaller than the within-subject variability during normal walking. Using a non-linear multibody dynamical model of swing leg dynamics, the hip, knee and ankle joint impedance were estimated at three time points during the swing phase for nine subjects walking at a speed of 0.5 m/s. The identified model was well able to predict the experimental responses for the hip and knee, since the mean variance accounted (VAF) for was 99% and 96%, respectively. The ankle lacked a consistent response and the mean VAF of the model fit was only 77%, and therefore the estimated ankle impedance was not reliable. The averaged across-subjects stiffness varied between the three time points within 34-66 and 0-3.5 Nm/rad Nm/rad for the hip and knee joint respectively. The damping varied between 1.9-4.6 and 0.02-0.14 Nms/rad Nms/rad for hip and knee respectively. The developed LOPER has a negligible effect on the unperturbed walking pattern and allows to identify hip and knee impedance during the swing phase.

Wearable Kinesthetic I/O Device for Sharing Wrist Joint Stiffness

In this paper, we developed a wearable kinesthetic I/O system, which is able to share wrist joint stiffness by measuring and intervening in four muscle activities on the forearm simultaneously through the same electrodes. This achieves interactive peg rehabilitation by sharing muscle activities among patients and therapists. Through the performance study, it was shown that 1) applied EMS and measured wrist joint stiffness, and 2) produced wrist joint stiffness and measured EMG value has a linear correlation, which allows designing a mapping function between the measured EMG value on one person and the EMS value applied to another person. In a perceptual study, which shared the wrist stiffness between two persons, participants were able to recognize the level of their confederate's wrist joint stiffness using a 4-point Likert scale linearly. The developed system would benefit a physical therapist and a patient for sharing their wrist stiffness and grip force, which are usually difficult to be observed in a visual contact.

Human Wrist Impedance Estimation Based on Impulse Response Induced by Snap-Through Buckling of Closed-Elastica

A human joint impedance estimation method employing a wearable device using the snap-through buckling of closed-elastica has been proposed in our previous study and tested for the ankle joint. In the present study, the method is applied to the wrist impedance estimation scheme. Our method aims at the task-dependent scenarios. The wrist joint is involved in a variety of tasks, where the impedance plays a key role in physical interaction with environments. Because of the unique design and actuation concept, the proposed device does not disrupt the voluntary movements of the wrist and can be used in the estimation during the user's own motion planning. In this paper, to verify our method, the wrist impedance under the quasi-stationary condition is estimated, and the results are compared with the related works. Also, we discuss the profiles of the induced impulse responses and suggest an alternative estimation manner which does not require any force (torque) information.

Design of physical user–robot interactions for model identification of soft actuators on exoskeleton robots

Recent breakthroughs in wearable robots, such as exoskeleton robots with soft actuators and soft exosuits, have enabled the use of safe and comfortable movement assistance. However, modeling and identification methods for soft actuators used in wearable robots have yet to be sufficiently explored. In this study, we propose a novel approach for obtaining accurate soft actuator models through the design of physical user–robot interactions for wearable robots, in which the user applies external forces to the robot. To obtain an accurate soft actuator model from the limited amount of data acquired through an interaction, we leverage an active learning framework based on Gaussian process regression. We conducted experiments using a two-degree-of-freedom upper-limb exoskeleton robot with four pneumatic artificial muscles (PAMs). Experimental results showed that physical interactions between the exoskeleton robot and the user were successfully designed to allow PAM models to be identified. Furthermore, we found that data acquired through an interaction could result in more accurate soft actuator models for the exoskeleton robots than data acquired without a physical interaction between the exoskeleton robot and the user.