Enhancing Walking Performance With a Bilateral Hip Exoskeleton Assistance in Individuals With Above-Knee Amputation

Transfemoral amputation is a debilitating condition that leads to long-term mobility restriction and secondary disorders that negatively affect the quality of life of millions of individuals worldwide. Currently available prostheses are not able to restore energetically efficient and functional gait, thus, recently, the alternative strategy to inject energy at the residual hip has been proposed to compensate for the lack of energy of the missing leg. Here, we show that a portable and powered hip exoskeleton assisting both the residual and intact limb induced a reduction of walking energy expenditure in four individuals with above-knee amputation. The reduction of the energy expenditure, quantified using the Physiological Cost Index, was in the range [-10, -17]% for all study participants compared to walking without assistance, and between [-2, -24]% in three out of four study participants compared to walking without the device. Additionally, all study participants were able to walk comfortably and confidently with the hip exoskeleton overground at both their self-selected comfortable and fast speed without any observable alterations in gait stability. The study findings confirm that injecting energy at the hip level is a promising approach for individuals with above-knee amputation. By reducing the energy expenditure of walking and facilitating gait, a hip exoskeleton may extend mobility and improve locomotor training of individuals with above-knee amputation, with several positive implications for their quality of life.

Walking-Speed-Adaptive Gait Phase Estimation for Wearable Robots

This paper introduces a Gait Phase Estimation Module (GPEM) and its real-time algorithm designed to estimate gait phases continuously and monotonically across a range of walking speeds and accelerations/decelerations. To address the challenges of real-world applications, we propose a speed-adaptive online gait phase estimation algorithm, which enables precise estimation of gait phases during both constant speed locomotion and dynamic speed changes. Experimental verification demonstrates that the proposed method offers smooth, continuous, and repetitive gait phase estimation when compared to conventional approaches such as the phase portrait method and time-based estimation. The proposed method achieved a 48% reduction in gait phase deviation compared to time-based estimation and a 48.29% reduction compared to the phase portrait method. The proposed algorithm is integrated within the GPEM, allowing for its versatile application in controlling gait assistive robots without incurring additional computational burden. The results of this study contribute to the development of robust and efficient gait phase estimation techniques for various robotic applications.

Continuous Phase Estimation in a Variety of Locomotion Modes Using Adaptive Dynamic Movement Primitives

Accurate gait phase estimation algorithms can be used to synchronize the action of wearable robots to the volitional user movements in real time. Current-day gait phase estimation methods are designed mostly for rhythmic tasks and evaluated in highly controlled walking environments (namely, steady-state walking). Here, we implemented adaptive Dynamic Movement Primitives (aDMP) for continuous real-time phase estimation in the most common locomotion activities of daily living, which are level-ground walking, stair negotiation, and ramp negotiation. The proposed method uses the thigh roll angle and foot-contact information and was tested in real time with five subjects. The estimated phase resulted in an average root-mean-square error of 3.98% ± 1.33% and a final estimation error of 0.60% ± 0.55% with respect to the linear phase. The results of this study constitute a viable groundwork for future phase-based control strategies for lower-limb wearable robots, such as robotic prostheses or exoskeletons.

Decoding Upper-Limb Movement Intention Through Adaptive Dynamic Movement Primitives: A Proof-of-Concept Study with a Shoulder-Elbow Exoskeleton

This work presents an intention decoding algorithm that can be used to control a 4 degrees-of-freedom shoulder-elbow exoskeleton in reaching tasks. The algorithm was designed to assist the movement of users with upper-limb impairments who can initiate the movement by themselves. It relies on the observation of the initial part of the user's movement through joint angle measures and aims to estimate in real-time the phase of the movement and predict the goal position of the hand in the reaching task. The algorithm is based on adaptive Dynamic Movement Primitives and Gaussian Mixture Models. The performance of the algorithm was verified in robot-assisted planar reaching movements performed by one healthy subject wearing the exoskeleton. Tests included movements of different amplitudes and orientations. Results showed that the algorithm could predict the hand's final position with an error lower than 5 cm after 0.25 s from the movement onset, and that the final position reached during the tests was on average less than 4 cm far from the target position. Finally, the effects of the assistance were observed in a reduction of the activation of the Biceps Brachii and of the time to execute the reaching tasks.

A gait phase prediction model trained on benchmark datasets for evaluating a controller for prosthetic legs

Powered lower-limb assistive devices, such as prostheses and exoskeletons, are a promising option for helping mobility-impaired individuals regain functional gait. Gait phase prediction plays an important role in controlling these devices and evaluating whether the device generates a gait similar to that of individuals with intact limbs. This study proposes a gait phase prediction method based on a deep neural network (DNN). The long short-term memory (LSTM)-based model predicts a continuous gait phase from the 250 ms history of the vertical load, thigh angle, knee angle, and ankle angle, commonly available on powered lower-limb assistive devices. One unified model was trained using publicly available benchmark datasets containing intact limb gaits for level-ground walking (LGW) and ascending stairs (SA). A phase prediction error of 1.28% for all benchmark datasets was obtained. The model was subsequently applied to a state machine-controlled powered prosthetic leg dataset collected from four individuals with unilateral transfemoral amputation. The gait phase prediction results (a phase prediction error of 5.70%) indicate that the model trained on benchmark data can be used for a system not included in the training dataset with no post-processing, such as model adaptation. Furthermore, it provided information regarding evaluation of the controller: whether the prosthetic leg generated normal gait. In conclusion, the proposed gait phase prediction model will facilitate efficient gait prediction and evaluation of controllers for powered lower-limb assistive devices.

Measurement, Evaluation, and Control of Active Intelligent Gait Training Systems—Analysis of the Current State of the Art

Gait recognition and rehabilitation has been a research hotspot in recent years due to its importance to medical care and elderly care. Active intelligent rehabilitation and assistance systems for lower limbs integrates mechanical design, sensing technology, intelligent control, and robotics technology, and is one of the effective ways to resolve the above problems. In this review, crucial technologies and typical prototypes of active intelligent rehabilitation and assistance systems for gait training are introduced. The limitations, challenges, and future directions in terms of gait measurement and intention recognition, gait rehabilitation evaluation, and gait training control strategies are discussed. To address the core problems of the sensing, evaluation and control technology of the active intelligent gait training systems, the possible future research directions are proposed. Firstly, different sensing methods need to be proposed for the decoding of human movement intention. Secondly, the human walking ability evaluation models will be developed by integrating the clinical knowledge and lower limb movement data. Lastly, the personalized gait training strategy for collaborative control of human–machine systems needs to be implemented in the clinical applications.