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BACKGROUND: In the recent past, wearable devices have been used for gait rehabilitation in patients with Parkinson's disease. The objective of this paper is to analyze the outcome of a wearable hip orthosis whose assistance adapts in real time to the patient's gait kinematics via adaptive oscillators. In particular, this study focuses on a metric characterizing natural gait variability, i.e., the level of long-range autocorrelations (LRA) in series of stride durations. METHODS: Eight patients with Parkinson's disease (Hoehn and Yahr stages 1[Formula: see text]2.5) performed overground gait training three times per week for four consecutive weeks, assisted by a wearable hip orthosis. Gait was assessed based on performance metrics such as the hip range of motion, speed, stride length and duration, and the level of LRA in inter-stride time series assessed using the Adaptive Fractal Analysis. These metrics were measured before, directly after, and 1 month after training. RESULTS: After training, patients increased their hip range of motion, their gait speed and stride length, and decreased their stride duration. These improvements were maintained 1 month after training. Regarding long-range autocorrelations, the population's behavior was standardized towards a metric closer to the one of healthy individuals after training, but with no retention after 1 month. CONCLUSION: This study showed that an overground gait training with adaptive robotic assistance has the potential to improve key gait metrics that are typically affected by Parkinson's disease and that lead to higher prevalence of fall. TRIAL REGISTRATION: ClinicalTrials.gov Identifer NCT04314973. Registered on 11 April 2020.

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In order to reduce the energy consumption of human daily movement without providing additional power, we considered the biomechanical behavior of the knee during external impedance interactions. Based on the theory of human sports biomechanics, combined with the requirements of human-machine coupling motion consistency and coordination, an unpowered exoskeleton-assisted device for the knee joint is proposed in this paper. The effectiveness of this assisted device was verified using gait experiments and distributed plantar pressure tests with three modes: "not wearing exoskeleton" (No exo.), "wearing exoskeleton with assistance " (Exo. On), and "wearing exoskeleton without assistance" (Exo. Off). The experimental results indicate that (1) This device can effectively enhance the function of the knee, increasing the range of knee movement by 3.72% (p < 0.001). (2) In the early stages of the lower limb swing, this device reduces the activity of muscles in relation to the knee flexion, such as the rectus femoris, vastus lateralis, and soleus muscles. (3) For the first time, it was found that the movement length of the plantar pressure center was reduced by 6.57% (p = 0.027). This basic principle can be applied to assist the in-depth development of wearable devices.

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A novel passive hip exoskeleton has been designed and built with the aim of reducing metabolic consumption during walking by a passive way of storing the negative mechanical energy in the deceleration phase and releasing it in the acceleration phase. A ratchet spiral spring mechanism with a set of double stable switches is designed inside the exoskeleton for the above purpose. An analysis is conducted on the mechanism and the switching timing for the energy management to automatically store or release the energy according to the biomechanics of walking. In addition, a gravity-balance mechanism embedded inside the exoskeleton is designed as well to minimize the influence of the lower limb weight on muscle work. Human-exoskeleton interaction has been studied using the Opensim software, and simulation results demonstrated the effectiveness of the exoskeleton in reducing metabolic consumption during walking. An exoskeleton prototype has been built and tested with experiments measuring assistive torque and surface electromyography signal, confirming the effectiveness of the gravity-balance mechanism and energy-storage method, as well as the exoskeleton's actual assistive effect.

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Many studies have demonstrated in the past that the level of long-range autocorrelations in series of stride durations, characterizing natural gait variability, is impacted by external constraints, such as treadmill or metronome, or by pathologies, such as Parkinson's or Huntington's disease. Nevertheless, no one has analyzed the effects on this metric of a gait constrained by a robot-mediated walking assistance, which intrinsically tends to normalize the gait pattern. This paper focuses on the influence of a wearable active pelvis orthosis on the level of long-range autocorrelations in series of stride durations. Ten healthy participants, aged between 55 and 77 yr, performed four overground walking sessions, wearing this orthosis, and with different assistive parameters. This study showed that the adaptive assistance provided by this device has the potential of improving gait metrics that are typically affected by aging, such as the hip range of motion, walking speed, stride length, and stride duration, without impacting natural gait variability, i.e., the level of long-range autocorrelations in series of stride durations. This combination is virtuous toward the design of an assistive device for people with locomotion disorders resulting in deteriorated levels of long-range autocorrelations, such as patients with Parkinson's disease.NEW & NOTEWORTHY This study is the first that investigates the effects of a wearable active pelvis orthosis using an oscillator-based adaptive assistance on the level of long-range autocorrelations in series of stride durations during overground walking. It is also the first to compare the effects of different assistance settings on spatiotemporal gait metrics.

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(1) Background: To enhance the walking-assistance efficiencies of exoskeletons, this paper proposed the biomechanical-based cooperativity model based on a passive exoskeleton prototype to fill the technical gap in exoskeleton design regarding the torque transmission law between humans and exoskeletons. (2) Methods: The cooperativity model was used to solve the key system parameters based on the minimum average dispersion degree, in which the average dispersion degree algorithm based on the joint angle was designed and applied. (3) Results: The influence of the cooperativity model on the exoskeleton was indicated by comparing the walking-assistance efficiencies of the exoskeletons with the same structure but with different elastic parameters of the energy storage components, in which the exoskeleton based on the cooperativity design exhibited the highest walking-assistance performance. The walking-assistance efficiency of the exoskeleton with the optimal parameter combinations was also tested by comparing the respiratory metabolisms with and without the exoskeleton, in which the exoskeleton provided the average walking-assistance efficiency of 14.45% for more than 80% of the subjects. (4) Conclusions: The effects of the cooperativity model on exoskeletons were proven, but the accuracy and efficiency of the model still have room for improvement, especially the accuracy of the offset principle.

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BACKGROUND: As an emerging exoskeleton robot technology, flexible lower limb exoskeleton (FLLE) integrates flexible drive and wearable mechanism, effectively solving many problems of traditional rigid lower limb exoskeleton (RLLE) such as higher quality, poorer compliance and relatively poor portability, and has become one of the important development directions in the field of active rehabilitation. OBJECTIVE: This review focused on the development and innovation process in the field of FLLE in the past decade. METHOD: Related literature published from 2010 to 2021 were searched in EI, IEEE Xplore, PubMed and Web of Science databases. Seventy target research articles were further screened and sorted through inclusion and exclusion criteria. RESULTS: FLLE is classified according to different driving modes, and the advantages and disadvantages of passive flexible lower limb exoskeletons and active flexible lower limb exoskeletons are comprehensively summarized. CONCLUSION: At present, FLLE's research is mainly based on cable drive, bionic pneumatic muscles followed and matured, and new exoskeleton designs based on smart material innovations also trend to diversify. In the future, the development direction of FLLE will be lightweight and drive compliance, and the multi-mode sensory feedback control theory, motion intention recognition theory and human-machine interaction theory will be combined to reduce the metabolic energy consumption of walking.

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Cerebrovascular accidents (CVA) cause a range of impairments in coordination, such as a spectrum of walking impairments ranging from mild gait imbalance to complete loss of mobility. Patients with CVA need personalized approaches tailored to their degree of walking impairment for effective rehabilitation. This paper aims to evaluate the validity of using various machine learning (ML) and deep learning (DL) classification models (support vector machine, Decision Tree, Perceptron, Light Gradient Boosting Machine, AutoGluon, SuperTML, and TabNet) for automated classification of walking assistant devices for CVA patients. We reviewed a total of 383 CVA patients' (1623 observations) prescription data for eight different walking assistant devices from five hospitals. Among the classification models, the advanced tree-based classification models (LightGBM and tree models in AutoGluon) achieved classification results of over 90% accuracy, recall, precision, and F1-score. In particular, AutoGluon not only presented the highest predictive performance (almost 92% in accuracy, recall, precision, and F1-score, and 86.8% in balanced accuracy) but also demonstrated that the classification performances of the tree-based models were higher than that of the other models on its leaderboard. Therefore, we believe that tree-based classification models have potential as practical diagnosis tools for medical rehabilitation.

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This paper introduces a novel exoskeleton active walking assistance control framework based on frequency adaptive dynamics movement primitives (FADMPs). The FADMPs proposed in this paper is an online learning and prediction algorithm which is able to online estimate the fundamental frequency of human joint trajectory, learn the shape of joint trajectory and predict the future joint trajectory during walking. The proposed active walking assistance control framework based on FADMPs is a model-based controller which relies on the human joint torque estimation. The assistance torque provided by exoskeleton is estimated by human lower limb inverse dynamics model which is sensitive to the noise in the joint motion trajectory. To estimate a smooth joint torque profile, the joint motion trajectory must be filtered first by a lowpass filter. However, lowpass filter will introduce an inevitable phase delay in the filtered trajectory. Both simulations and experiments in this paper show that the phase delay has a significant effect on the performance of exoskeleton active assistance. The active assistant control framework based on FADMPs aims at improving the performance of active assistance control by compensating the phase delay. Both simulations and experiments on active walking assistance control show that the performance of active assistance control can be further improved when the phase delay in the filtered trajectory is compensated by FADMPs.

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To alleviate the influence of aging on the elderly, a hip-knee coupling exoskeleton with offset theory is designed in this article to improve people's athletic ability. With the novel design, the unique application strategy of the offset principle for hip and knee joints is developed, and a hip-knee coupling mechanism is proposed to solve the discrete power assistance problem for the knee joint. To acquire the success of the design, the mathematical model of the coupling mechanism is established to optimize the load environment of the exoskeleton system, and furthermore, an algorithm adapted to human movement is proposed to determine the monotonicity of the cam profiles. For the selection of the elastic parameters in the coupling mechanism, the sensitivity condition is proposed, and the human-machine interaction model of the KESM is further established. A man-machine coupling model was used to verify the scientificity of the exoskeleton design, and the comparison between the joint powers with or without exoskeleton indicated that the exoskeleton theoretically saved at least 20% of the human body's energy.

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Lower limb robotic exoskeletons have shown the capability to enhance human locomotion for healthy individuals or to assist motion rehabilitation and daily activities for patients. Recent advances in human-in-the-loop optimization that allowed for assistance customization have demonstrated great potential for performance improvement of exoskeletons. In the optimization process, subjects need to experience multiple types of assistance patterns, thus, leading to a long evaluation time. Besides, some patterns may be uncomfortable for the wearers, thereby resulting in unpleasant optimization experiences and inaccurate outcomes. In this study, we investigated the effectiveness of a series of ankle exoskeleton assistance patterns on improving walking economy prior to optimization. We conducted experiments to systematically evaluate the wearers' biomechanical and physiological responses to different assistance patterns on a lightweight cable-driven ankle exoskeleton during walking. We designed nine patterns in the optimization parameters range which varied peak torque magnitude and peak torque timing independently. Results showed that metabolic cost of walking was reduced by 17.1 ± 7.6% under one assistance pattern. Meanwhile, soleus (SOL) muscle activity was reduced by 40.9 ± 19.8% with that pattern. Exoskeleton assistance changed maximum ankle dorsiflexion and plantarflexion angle and reduced biological ankle moment. Assistance pattern with 48% peak torque timing and 0.75 N·m·kg -1 peak torque magnitude was effective in improving walking economy and can be selected as an initial pattern in the optimization procedure. Our results provided a preliminary understanding of how humans respond to different assistances and can be used to guide the initial assistance pattern selection in the optimization.

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More recently, lower limb exoskeletons (LLE) have gained considerable interests in strength augmentation, rehabilitation, and walking assistance scenarios. For walking assistance, the LLE is expected to control the affected leg to track the unaffected leg's motion naturally. A critical issue in this scenario is that the exoskeleton system needs to deal with unpredictable disturbance from the patient, and the controller has the ability to adapt to different wearers. To this end, a novel data-driven optimal control (DDOC) strategy is proposed to adapt different hemiplegic patients with unpredictable disturbances. The interaction relation between two lower limbs of LLE and the leg of patient's unaffected side are modeled in the context of leader-follower framework. Then, the walking assistance control problem is transformed into an optimal control problem. A policy iteration (PI) algorithm is utilized to obtain the optimal controller. To improve the online adaptation to different patients, an actor-critic neural network (AC/NN) structure of the reinforcement learning (RL) is employed to learn the optimal controller on the basis of PI algorithm. Finally, experiments both on a simulation environment and a real LLE system are conducted to verify the effectiveness of the proposed walking assistance control method.

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Propulsion during push-off (PO) is a key factor to realize human locomotion. Through the detection of real-time gait stage, assistance could be provided to the human body at the proper time. In most cases, ankle-foot exoskeletons consist of electronic sensors, microprocessors, and actuators. Although these three essential elements contribute to fulfilling the function of the detection, control, and energy injection, they result in a huge system that reduces the wearing comfort. To simplify the sensor-controller system and reduce the mass of the exoskeleton, we designed a smart clutch in this paper, which is a sensor-controller integrated system that comprises a sensing part and an executing part. With a spring functioning as an actuator, the whole exoskeleton system is completely made up of mechanical parts and has no external power source. By controlling the engagement of the actuator based on the signal acquired from the sensing part, the proposed clutch enables the ankle-foot exoskeleton (AFE) to provide additional ankle torque during PO, and allows free rotation of the ankle joint during swing phase, thus reducing the metabolic cost of the human body. There are two striking advantages of the designed clutch. On the one hand, the clutch is lightweight and reliable-it resists the possible shock during walking since there is no circuit connection or power in the system. On the other hand, the detection of gait relies on the contact states between human feet and the ground, so the clutch is universal and does not need to be customized for individuals.

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Assistive devices can be considered as one of the main applications of legged locomotion research in daily life. In order to develop an efficient and comfortable prosthesis or exoskeleton, biomechanical studies on human locomotion are very useful. In this paper, the applicability of the FMCH (force modulated compliant hip) model is investigated for control of lower limb wearable exoskeletons. This is a bioinspired method for posture control, which is based on the virtual pivot point (VPP) concept, found in human walking. By implementing the proposed method on a detailed neuromuscular model of human walking, we showed that using a biarticular actuator parallel to the hamstring muscle, activation in most of the leg muscles can be reduced. In addition, the total metabolic cost of motion is decreased up to 12%. The simple control rule of assistance is based on leg force feedback which is the only required sensory information.

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We present a novel assistive control strategy for a robotic hip exoskeleton for assisting hip flexion/extension, based on a proportional Electromyography (EMG) strategy. The novelty of the proposed controller relies on the use of the Gastrocnemius Medialis (GM) EMG signal instead of a hip flexor muscle, to control the hip flexion torque. This strategy has two main advantages: first, avoiding the placement of the EMG electrodes at the human-robot interface can reduce discomfort issues for the user and motion artifacts of the recorded signals; second, using a powerful signal for control, such as the GM, could improve the reliability of the control system. The control strategy has been tested on eight healthy subjects, walking with the robotic hip exoskeleton on the treadmill. We evaluated the controller performance and the effect of the assistance on muscle activities. The tuning of the assistance timing in the controller was subject dependent and varied across subjects. Two muscles could benefit more from the assistive strategy, namely the Rectus Femoris (directly assisted) and the Tibialis Anterior (indirectly assisted). A significant correlation was found between the timing of the delivered assistance (i.e., synchronism with the biological hip torque), and reduction of the hip flexors muscular activity during walking; instead, no significant correlations were found for peak torque and peak power. Results suggest that the timing of the assistance is the most significant parameter influencing the effectiveness of the control strategy. The findings of this work could be important for future studies aimed at developing assistive strategies for walking assistance exoskeletons.

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The aim of this study was to investigate the reliability of temporal and spatial gait parameters in patients dependent on walking assistance after severe stroke, and to examine agreement between simple and advanced methods. Twenty-one patients, admitted for in-patient multidisciplinary rehabilitation, were assessed repeatedly for walking function, both in a test corridor and a gait laboratory (3D camera system) before and after 11 weeks of rehabilitation. The test-retest reliability was examined using intraclass correlation (ICC1.1), and measurement error was reported by within-subject standard deviation (Sw). The agreement between different methods for assessing walking speed, cadence and step length was explored by Bland-Altman plots. High to excellent test-retest reliability was found between trials, both when assessed in the corridor (ICC: 0.93-0.99) and in the laboratory (ICC: 0.88-0.99). Agreement between methods was satisfactory at baseline and was higher after the rehabilitation period. Agreement was found to be slightly better at lower walking speeds and for shorter step lengths. The results implicate that temporal-spatial gait parameters may be measured reliably by both simple and advanced methods in dependent walkers after stroke. A high level of agreement was found between the two methods for walking speed, cadence and average step length at both test points.

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