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Enhance human walking and running is much more difficult compared to build a machine to help someone with disability. Unpowered ankle-foot exoskeletons are the current development trend due to their lightweight, wearable, and energy-free features, but the huge recognition and energy control system still affects their practicability. To refine the recognition and control system, we designed an unpowered soft ankle-foot exoskeleton with a purely mechanical self-adaptiveness clutch, which can realize the collection and release of energy according to different gait stage. Through switching and closing of this clutch, energy is collected when the ankle is doing negative work and released when the ankle is doing positive work. Results shows the unpowered ankle-foot exoskeleton at the stiffness of 12000 N/m could relieve muscles' load, with reduction of force by 52.3 % and 5.2%, and of power by 44.2% and 7.0%, respectively for soleus and gastrocnemius in simulation.Clinical Relevance-The proposed Unpowered Ankle-Foot Exoskeleton can both reduce muscle forces and powers. Hence, it can be used to assist walking of the elderly, others with neurocognitive disorders or leg diseases.

A modular rehabilitation exoskeleton robot for hemiplegic patients (SIAT-H) is created in this paper. The design of the exoskeleton is based on the gait analysis of hemiplegic patients and the movement data of healthy people. Through the motion capture experiments at different speeds, the walking style of patients with hemiplegia is understood. A modular exoskeleton robot, including an independent hip joint, knee joint and ankle joint, is designed. The mechanical structure which is optimized based on the physiological characteristics of human body would not cause additional damage to the joints during walking. In order to ensure the reliability of use, the structural strength is also considered. The kinematics relationship is verified by comparison of simulation data and calculated data. In the end, we made an experimental prototype, and the wearing experiment verified the reliability of the mechanism.

Phonation is generated by sequentially closing and opening of vocal folds, which is controlled by the laryngeal musculature. Pitch that is the highest contributor to generate natural voice depends on the contraction of muscles over the neck. Proper evaluation of muscular activities from neck muscles would be helpful to understand the phonation functions. While surface electromyography (sEMG) technique has been chosen to investigate physiological characteristics of neck muscles for pitch estimation in previous studies, it remains unclear what are the contracting patterns on both left and right sides of the neck muscles. In this study, 80 channels of high-density (HD) sEMG electrodes were recorded from three healthy volunteers when they phonated vowel (/a/) during varying pitch from normal to high. The root mean square (RMS) of the HD sEMG signals was computed within a series of analysis windows. An averaged RMS was used to construct HD sEMG energy maps for offering average energy distribution of the neck muscles. Normalized RMS (NRMS) was calculated to quantitatively display the correlation of the neck muscles between the left and right sides. The results of sEMG waveforms and NRMS values showed obvious symmetry between the left and right sides of the front neck muscles. Additionally, the NRMS across all five rows revealed a left-right significant correlation (r=0.9432 ± 0.018, p

Pattern recognition of limb motion based electromyography (EMG) is a core technique in active prosthesis control. By decoding different EMG signal, we can obtain different motion intentions. As a kind of EMG signal, surface electromyogram signal (sEMG) signal are widely used in research for its non-invasive characteristic. How to collect sEMG signal for recognition effectively is momentous part for EMG signal decoding. For this, we design two methods of collecting sEMG signal, which according to the situation in the experiment. One method is to repeatedly collect the same action until all the interested actions are collected. Another method of sample is to acquire the interested movements randomly. Ultimately, we find that the average recognition rates of these two methods are 96.9% and 97.5% respectively. These two methods have no significant effect on the final classification results. Therefore, in the experiment, we can take a more convenient method of continuous action acquisition to obtain sEMG signal. At the same time, the classification results are sent to the control system for verification, which we obtained a high quality recognition results. This also indirectly verifies the usability of the EMG acquisition system we designed.