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Individuals with Transfemoral Amputation (TFA) usually exhibit hip hiking walking with passive prostheses due to insufficient knee flexion. Powered knee prostheses can provide net-positive energy in the knee joint with improved gait symmetry. However, their effects on improving hip hiking are not well understood. This study investigates residual hip hiking through walking experiments using a robotic transfemoral prosthesis that can provide swing assistance. Different levels of assistive flexion torque were provided during the pre-swing and the mid-swing (no assistance, low assistance, medium assistance, high assistance). The kinematic and electromyographic (EMG) results indicate that with the increasing assistive torque level applied, the residual hip hiking was improved. The results preliminarily demonstrate the positive effect of knee flexion assistance on hip hiking improvement.

The rupture of the Achilles Tendon (AT) is a prevalent affliction among athletic populations. The patients who process AT repair frequently exhibit a deficit in the activation and strength of the gastrocnemius muscle. The exoskeleton can help with the patients gait training, especially perform effectively interventions during walking. However, the ankle exoskeleton is not suitable for acute-phase gait training, and the knee exoskeleton may have the possibility of downward migration. To address this issue and facilitate the early-stage rehabilitation of the gastrocnemius, this paper proposes a trans-multi-articular gait intervention strategy. Preliminary study on healthy subjects suggests that the implementation of extension resistance during the hip extension phase of the gait cycle can contribute to an increment in the strength of the ankle plantar flexion muscle. Future research will focus on the effectiveness of the proposed therapy strategy in practical application to patients.

Cardiovascular diseases have become a severe threat to human health. Fortunately, most of them can be effectively assessed and prevented through long-term monitoring of cardiovascular signals. Wearable medical sensors play an essential role in monitoring human physiological health, which are heading towards ultra-low power consumption, high sensitivity and stability. Furthermore, a comfortable wearable sensor also needs to be flexible and breathable. Here, a self-powered textile pulse sensor (STPS) based on triboelectric nanogenerator (TENG) is demonstrated for real-time monitoring of the radial artery pulse waveform. STPS can directly convert tiny pressure signals into electrical signals with excellent linearity (R 2 = 0.996), low detection limit, and long-term stable performance (5×10 4 cycles). The flexible textile-based STPS can be conformally attached to the human body for continuously and stably recording physiological mechanical signals, which is expected to be utilized in the personalized cardiovascular pulse monitoring wearable devices in the Internet of Things era.

Human-machine interface with muscle signals serves as an important role in the field of wearable robotics. To compensate for the limitations of the existing surface Electromyography (sEMG) based technologies, we previously proposed a noncontact capacitive sensing approach that could record the limb shape changes. The sensing approach frees the human skin from contacting to the metal electrodes, thus enabling the measurement of muscle signals by dressing the sensing front-ends outside of the clothes. We validated the capacitive sensing in human motion intent recognition tasks with the wearable robots and produced comparable results to existing studies. However, the biological significance of the capacitance signals is still unrevealed, which is an indispensable issue for robot intuitive control. In this study, we address the problems of identifying the relationships between the muscle morphological parameters and the capacitance signals. We constructed a measurement system that recorded the noncon-tact capacitive sensing signals and the muscle ultrasound (US) images simultaneously. With the designed device, five subjects were employed and the US images from the gastrocnemius muscle (GM) and the tibialis anterior (TA) muscle during level walking were sampled. We fitted the calculated muscle morphological parameters (the pinnation angles and the muscle fascicle length) and the capacitance signals of the same gait phases. The results demonstrated that at least one-channel capacitance signal strongly correlated to the muscle morphological parameters (R 2 > 0.5, quadratic regression). The average R 2 s of the most correlated channels were up to 0.86 for pinnation angles and 0.83 for the muscle fascicle length changes. The interesting findings in this preliminary study suggest the biological physical significance of the capacitance signals during human locomotion. Future efforts are worth being paid in this new research direction for more promising results.

In this study, we proposed a continuous stroke phase recognition method with lower-limb inertial signals. The aim of the method was to decrease the time needed and to relieve the burdensome manual configurations in the tasks of human underwater motion recognition. The method automatically segmented the data of a period of time into stroke cycles and three sub-phases (propulsion, glide and recovery). K-nearest neighbor algorithm (k-NN) was used as the classifier to train the segmented data and classify the new data on each sample interval. To validate the proposed recognition method, three elite swimmers were recruited. We also designed an wearable sensing system for human underwater motion sensing with inertial measurement units (IMUs). With only data of 5 stroke cycles for training, the recognizer produced accurate recognition results. The average precision across the phases and the subjects was 93.7% and the average recall was 92.6%. We also investigated the time difference of the key stroke events (stroke phase transitions) between the recognized decisions and the reference ones. The average time difference was 66.2 ms, which accounted for the 4.2% of a single stroke phase. The results of the pilot study proved the feasibility of the new method for human aquatic locomotion assistance tasks. Future efforts will be paid in this new direction for more promising results.

To achieve smooth locomotion transitions, locomotion intent prediction is very important for the control of knee exoskeleton. In this study, we develop a multi-sensor based locomotion intent prediction system based on Support Vector Machine (SVM), which can identify the current locomotion mode (sit, sit-to-stand, stand, level-ground walking, or stand-to-sit) and detect the locomotion transition between these modes onboard online. Two IMUs are mounted on the unilateral front of thigh part and shank part of the knee exoskeleton, and each of them generates 9 channels data. To evaluate the performance of this prediction system, several experiments are conducted on five healthy subjects. Average recognition accuracy is 96.89% ± 0.23%. Most transitions can be detected before the onsets of the transitions and no missed detections are observed for all the trials of the five able-bodied subjects.

This study explores the relationships between noncontact capacitive sensing signals and continuous grasp forces. It is a crucial step towards the volitional control of robotic systems based on the noncontact sensing approach. We firstly designed a measurement system including the capacitive sensing front-ends, the grasp force sensor, the signal sampling circuits and the graphic user interface. The capacitive sensing front-end was specifically designed for human forearm signal sampling, which was worn outside of the clothes. After implementation of the system, we carried out experiments on five healthy subjects, and the sensing bands were customized with their arm shapes. The grasp force and the capacitance signals were record simultaneously when the subjects gradually increased the force according to instruction. Linear regression and quadratic regression were used to evaluate the regulated signals. For each subject, at least one channel of capacitance signals were linear correlated to the normalized grasp force with ${{R}^{2}}\ge 0.85$. We found there was inter-subject similarity on the capacitance-force relationships. Cross validation on grasp force estimation with capacitance signals were also carried out, and the average relative estimation error was about 18%. The results proved the feasibility of the noncontact capacitive sensing method for human joint force estimation.

In this paper, we present a fuzzy-logic-based hybrid locomotion mode classification method for an active pelvis orthosis. Locomotion information measured by the onboard hip joint angle sensors and the pressure insoles is used to classify five locomotion modes, including two static modes (sitting, standing still), and three dynamic modes (level-ground walking, ascending stairs, and descending stairs). The proposed method classifies these two kinds of modes first by monitoring the variation of the relative hip joint angle between the two legs within a specific period. Static states are then classified by the time-based absolute hip joint angle. As for dynamic modes, a fuzzy-logic based method is proposed for the classification. Preliminary experimental results with three able-bodied subjects achieve an off-line classification accuracy higher than 99.49%.

Affording lower-limb amputees the ability to volitionally control robotic prostheses can improve the adaptability to terrain changes as well as enhancing proprioception. However, it also increases amputees' conscious burdens for prosthesis control. Therefore, in this paper, we aim to propose a hybrid controller which combines human volitional control with the intrinsic controller on the robotic transtibial prosthesis, enabling the amputee actively controlling prosthesis with little conscious attention. In this preliminary study, a hybrid controller for adaptive slope walking was designed. A slope estimator was embedded in the intrinsic controller to estimate the ground slope of the previous step using signals measured by prosthetic sensors. And a myoelectric controller allows the amputee subject to convey slope changes to prosthetic controller by volitionally contract his residual muscles, whose electromyography signals were mapped to the slope increment. The hybrid controller combined these two results to obtain the estimated slope. One male transtibial amputee subject was recruited in this research. Experiment results showed that the intrinsic slope estimator produced satisfactory estimation results with an average absolute error of 0.70 ± 0.54 degrees. By adding amputee's volitional control, the hybrid controller is able to predict the upcoming slope changes.

Improving the intuitiveness of the interaction between human and machine is an important issue for powered lower-limb prosthesis control. In this research, we aimed to evaluate the potential of using surface electromyography (EMG) signals measured from transtibial amputees' residual muscles to directly control the position of prosthetic ankle. In this research, one transtibial amputee subject and five able-bodied subjects were recruited. They were asked to control a virtual ankle to reach different target positions. The amputee subject finished these tasks in an average time of 1.29 seconds for different target positions with the residual limb, which was comparable with that using the amputee's sound limb and those with able-bodied subjects' dominant legs. Due to human's strong adaptability, the amputee subject was able to adapt to the control model trained one day before or trained in a posture which was different from that during performing control tasks. These results validate the promise of using surface EMG signals to volitionally control powered transtibial prostheses.

Ankle joint with spasticity and/or contracture can severely affect mobility and independence of stroke survivors. Due to that, the Achilles tendon(AT) is affected. In this paper, we aim to study changes of AT properties via proprioceptive neuromuscular facilitation (PNF) treatment. A robotic ankle-foot rehabilitation system has been proposed, which consists of a robotic ankle-foot platform and a graphic user interface. In this pilot study, two post-stroke patients participated and carried out a 12-week PNF treatment with the robotic system. The treatment is evaluated quantitatively in AT properties. The evaluation shows that after the PNF treatment, the average decrease of AT length is 4.1 mm (6.5%) and the recovery ratio is 30.4%, while the thickness has no change. The results indicate that the PNF based robotic rehabilitation for ankle joints with spasticity and/or contracture is effective to improve the ankle spasticity/contracture.