The aging population is now a global challenge, and impaired walking ability is a common feature in the elderly. In addition, some occupations such as military and relief workers require extra physical help to perform tasks efficiently. Robotic hip exoskeletons can support ambulatory functions in the elderly and augment human performance in healthy people during normal walking and loaded walking by providing assistive torque. In this review, the current development of robotic hip exoskeletons is presented. In addition, the framework of actuation joints and the high-level control strategy (including the sensors and data collection, the way to recognize gait phase, the algorithms to generate the assist torque) are described. The exoskeleton prototypes proposed by researchers in recent years are organized to benefit the related fields realizing the limitations of the available robotic hip exoskeletons, therefore, this work tends to be an influential factor with a better understanding of the development and state-of-the-art technology.

Introduction: Human-in-the-loop optimization has made great progress to improve the performance of wearable robotic devices and become an effective customized assistance strategy. However, a lengthy period (several hours) of continuous walking for iterative optimization for each individual makes it less practical, especially for disabled people, who may not endure this process. Methods: In this paper, we provide a muscle-activity-based human-in-the-loop optimization strategy that can reduce the time spent on collecting biosignals during each iteration from around 120 s to 25 s. Both Bayesian and Covariance Matrix Adaptive Evolution Strategy (CMA-ES) optimization algorithms were adopted on a portable hip exoskeleton to generate optimal assist torque patterns, optimizing rectus femoris muscle activity. Four volunteers were recruited for exoskeleton-assisted walking trials. Results and Discussion: As a result, using human-in-the-loop optimization led to muscle activity reduction of 33.56% and 41.81% at most when compared to walking without and with the hip exoskeleton, respectively. Furthermore, the results of human-in-the-loop optimization indicate that three out of four participants achieved superior outcomes compared to the predefined assistance patterns. Interestingly, during the optimization stage, the order of the two typical optimizers, i.e., Bayesian and CMA-ES, did not affect the optimization results. The results of the experiment have confirmed that the assistance pattern generated by muscle-activity-based human-in-the-loop strategy is superior to predefined assistance patterns, and this strategy can be achieved more rapidly than the one based on metabolic cost.

With the help of wearable robotics, the lower limb exoskeleton becomes a promising solution for spinal cord injury (SCI) patients to recover lower body locomotion ability. However, fewer exoskeleton gait planning methods can meet the needs of patient in real time, e.g., stride length or step width, etc., which may lead to human-machine incoordination, limit comfort, and increase the risk of falling. This work presents a human-exoskeleton-crutch system with the center of pressure (CoP)-based gait planning method to enable the balance control during the exoskeleton-assisted walking with crutches. The CoP generated by crutches and human-machine feet makes it possible to obtain the overall stability conditions of the system in the process of exoskeleton-assisted quasi-static walking, and therefore, to determine the next stride length and ensure the balance of the next step. Thus, the exoskeleton gait is planned with the guidance of stride length. It is worth emphasizing that the nominal reference gait is adopted as a reference to ensure that the trajectory of the swing ankle mimics the reference one well. This gait planning method enables the patient to adaptively interact with the exoskeleton gait. The online gait planning walking tests with five healthy volunteers proved the method’s feasibility. Experimental results indicate that the algorithm can deal with the sensed signals and plan the landing point of the swing leg to ensure balanced and smooth walking. The results suggest that the method is an effective means to improve human–machine interaction. Additionally, it is meaningful for the further training of independent walking stability control in exoskeletons for SCI patients with less assistance of crutches.

Nowadays, wearable devices mainly exist in the form of portable accessories with various functions, connecting various kinds of terminals like mobile phones to form various wearable systems. In a wearable system, the wearable power supply device is the key component as energy dispenser for all devices. Nanosheets, a kind of two-dimensional material, which always displays a high surface-to-volume ratio and thus is lightweight and has remarkable conductive as well as electrochemical properties, have become the optimal choice for wearable power supply devices. The development and status of nanosheet-based wearable power supply devices including nanosheet-based wearable batteries, nanosheet-based wearable supercapacitors, nanosheet-based wearable self-powered energy suppliers are introduced in this article. Besides, the future opportunities and challenges of wearable devices are discussed.

Exoskeleton robots hold promising prospects for rehabilitation training in individuals with weakened muscular conditions. However, achieving improved human–machine interaction and delivering customized assistance remains a challenging task. This paper introduces a muscle synergy-based human-in-the-loop (HIL) optimization framework for hip exoskeletons to offer more personalized torque assistance. Initially, we propose a muscle synergy similarity index to quantify the similarity of synergy while walking with and without the assistance of an exoskeleton. By integrating surface electromyography (sEMG) signals to calculate metrics evaluating muscle synergy and iteratively optimizing assistance parameters in real time, a muscle synergy-based HIL optimized torque configuration is presented and tested on a portable hip exoskeleton. Iterative optimization explores the optimal and suboptimal assistance torque profiles for six healthy volunteers, simultaneously testing zero torque and predefined assistance configurations, and verified the corresponding muscle synergy similarity indices through experimental testing. In our validation experiments, the assistance parameters generated through HIL optimization significantly enhance muscle synergy similarity during walking with exoskeletal assistance, with an optimal average of 0.80 ± 0.04 (mean ± std), marking a 6.3% improvement over prior assistive studies and achieving 96.4% similarity compared with free walking. This demonstrates that the proposed muscle synergy-based HIL optimization can ensure robotic exoskeleton-assisted walking as “natural” as possible.

Background: Pathological gaits of children may lead to terrible diseases, such as osteoarthritis or scoliosis. By monitoring the gait pattern of a child, proper therapeutic measures can be recommended to avoid the terrible consequence. However, low-cost systems for pathological gait recognition of children automatically have not been on market yet. Our goal was to design a low-cost gait-recognition system for children with only pressure information.Methods: In this study, we design a pathological gait-recognition system (PGRS) with an 8 × 8 pressure-sensor array. An intelligent gait-recognition method (IGRM) based on machine learning and pure plantar pressure information is also proposed in static and dynamic sections to realize high accuracy and good real-time performance. To verifying the recognition effect, a total of 17 children were recruited in the experiments wearing PGRS to recognize three pathological gaits (toe-in, toe-out, and flat) and normal gait. Children are asked to walk naturally on level ground in the dynamic section or stand naturally and comfortably in the static section. The evaluation of the performance of recognition results included stratified tenfold cross-validation with recall, precision, and a time cost as metrics.Results: The experimental results show that all of the IGRMs have been identified with a practically applicable degree of average accuracy either in the dynamic or static section. Experimental results indicate that the IGRM has 92.41% and 97.79% intra-subject recognition accuracy, and 85.78% and 78.81% inter-subject recognition accuracy, respectively, in the static and dynamic sections. And we find methods in the static section have less recognition accuracy due to the unnatural gesture of children when standing.Conclusions: In this study, a low-cost PGRS has been verified and realize feasibility, highly average precision, and good real-time performance of gait recognition. The experimental results reveal the potential for the computer supervision of non-pathological and pathological gaits in the plantar-pressure patterns of children and for providing feedback in the application of gait-abnormality rectification.