A hybrid modulation strategy is proposed for the LCC – LCC compensated bidirectional wireless power transfer system ( LCC -BWPT), which aims to boost the efficiencies in the whole operating range. The basic operating modes of LCC -BWPT with the hybrid modulation strategy are analyzed, and the unified discrete-time model is then established based on the full-mode operation, which can accurately describe the state variables under all operating modes. Based on the model, the criteria for optimal operation are put forward in consideration of three working processes of LCC -BWPT, i.e., the constant current (CC) charging process, the constant voltage charging process, and the CC discharging process. The optimal control variables in three working processes are derived, respectively, which realize full zero-voltage switching operation and minimize the reactive current. On this basis, an optimal modulation strategy featuring high efficiency performance is proposed for LCC -BWPT. Besides, a control method independent of the real-time wireless communication is developed for LCC -BWPT with the optimal modulation strategy. Finally, a 1.2 kW prototype is applied, and experimental results verify the effectiveness of the proposed optimal modulation strategy, where the efficiencies are improved in the whole operating range.

Motor impairment after stroke is generally caused by damage to the neural networks that control movement. Corticomuscular coherence (CMC) is a valid method to analyze the functional connectivity of the corticospinal pathway between the cerebral cortex andmuscles. However, current studies on CMC in stroke patients only focused on the upper limbs. The functional connectivity between the brain and lower limbs in stroke patients has not been well studied. Therefore, twelve stroke patients and fifteen healthy controls were recruited and their electroencephalogram (EEG) and electromyogram (EMG) of Tibialis Anterior (TA), Lateral Gastrocnemius (LG) and Medial Gastrocnemius (MG) during unilateral static ankle dorsiflexion were recorded. We found the mean beta and gamma CMC values of Cz electrode of stroke patients were significantly lower than those of healthy controls (p < 0.05). The brain topography showed significant coherence in the center of the cerebral cortex in healthy controls, while there was no significant coherence in stroke patients. For clinical assessment, there was a significant positive correlation between CMC and lower limb Fugl-Meyer Assessment (FMA) for Cz-TA in beta band (r = 0.6296, p = 0.0282), Cz-LG in beta band (r = 0.6816, p = 0.0147), and Cz-MG in gamma band (r = 0.6194, p = 0.0317). A multiple linear regression model was established between CMC and lower limb FMA (R ² = 0.6600, p = 0.0280). Therefore, CMC between the cerebral cortex and lower limb muscles may be used as a new rehabilitation assessment biomarker in stroke.

Locomotion recognition and prediction is essential for real-time human–machine interactive control. The integration of electromyography (EMG) with mechanical sensors could improve the performance of locomotion recognition. However, the potential of EMG in motion prediction is rarely discussed. This paper firstly investigated the effect of surface EMG on the prediction of locomotion while integrated with inertial data. We collected EMG signals of lower limb muscle groups and linear acceleration data of lower limb segments from ten healthy participants in seven locomotion activities. Classification models were built based on four machine learning methods—support vector machine (SVM), k-nearest neighbor (KNN), artificial neural network (ANN), and linear discriminant analysis (LDA)—where a major vote strategy and a content constraint rule were utilized for improving the online performance of the classification decision. We compared four classifiers and further investigated the effect of data fusion on the online locomotion classification. The results showed that the SVM model with a sliding window size of 80 ms achieved the best recognition performance. The fusion of EMG signals does not only improve the recognition accuracy of steady-state locomotion activity from 90% (using acceleration data only) to 98% (using data fusion) but also enables the prediction of the next steady locomotion (∼370 ms). The study demonstrates that the employment of EMG in locomotion recognition could enhance online prediction performance.

Patients with early-stage Parkinson’s disease (PD) exhibit various but subtle motor symptoms, especially postural instability and gait disorders (PIGD). Patients show deteriorated gait performance at turns as the complex gait task requires more limb coordination and postural stability control, which may help to discriminate signs of early PIGD. In this study, we firstly proposed an IMU-based gait assessment model for quantifying comprehensive gait variables in both straight walking and turning tasks from five domains: respectively gait spatiotemporal parameters, joint kinematic parameters, variability, asymmetry, and stability. Twenty-one patients with idiopathic Parkinson’s disease at the early stage and nineteen age-matched healthy elderly adults were enrolled in the study. Each participant wore a full-body motion analysis system with 11 inertial sensors and walked along a path consisting of straight walking and 180-degree turns at a self-comfortable speed. A total of one hundred and thirty-nine gait parameters were derived for each gait task. We explored the factor effect of group and gait tasks on gait parameters using a two-way mixed analysis of variance. The discriminating ability of gait parameters between PD and the control group was evaluated using receiver operating characteristic analysis. Sensitive gait features were optimally screened (AUC>0.7) and categorized into 22 groups to classify PD and healthy controls based on a machine learning method. Results demonstrated that PD patients exhibited more gait abnormalities at turns, especially on the RoM and stability of the neck, shoulder, pelvic, and hip joints compared to the healthy control group. These gait metrics have good discriminating abilities to identify early-stage PD (AUC>0.65). Moreover, the inclusion of gait features at turns can significantly improve the classification accuracy compared to that only used parameters during straight walking. We show that quantitative gait metrics during turning have great potential to be used for enhancing early-stage PD detection.

Turning while walking has a frequent occurrence in daily life. Evaluation of its dynamic stability will facilitate fall prevention and rehabilitation scheme. This knowledge is so limited that we set it as the first aim of this study. Another aim was to investigate spatiotemporal parameters during turning. Fifteen healthy young adults were instructed to perform straight walking, 45° step turn to the left and 45° spin turn to the right at natural speed. Dynamic stability was measured by margin of stability (MoS) in anterior, posterior, left and right direction at each data point where significant differences were detected using 95% bootstrap confidence band. Common spatiotemporal parameters were computed in each condition subdivided into approach, turn and depart phases. Results showed that minimum anterior MoS appeared at middle of swing while minimum lateral MoS at contralateral heel strike in all conditions. Posterior MoS decreased before middle of turn phase in spin whereas after middle of turn phase in step. Lateral MoS and stride width declined in turn phase of spin while in depart of step. Spin had a long step and stride length. Long swing phases were observed in turns. These data help explain that people are most likely to fall forward at middle of swing and to fall toward the back and the support side at heel strike. Our findings demonstrate that instability mainly exist in turn phase of spin and depart phase of step turn.

Inertial measurement unit (IMU) has been widely applied in the development of upper-limb motion assessment model due to its portability, low cost and theoretically unlimited workspace. However, most inertial-based models do not meet the accuracy requirement of clinical use. The errors mainly arise from the sensor measurement and “sensor-to-segment” misalignment. This paper presented a novel inertial-based upper-limb motion assessment model with two layers which enabled simultaneous estimation of the joint angles and positions. The sensor-to-segment dynamic calibration method was employed in the construction of the upper limb kinematic model. The joint angles were calculated based on orientation transforms between adjacent body segments while the joint positions were estimated using a forward kinematic chain. The model performance was evaluated with the terms of the Pearson Correlation Coefficent (PCC), offset, and root-mean-square error (RMSE) by comparing to results derived from the Vicon reference system. Effects of motion type and motion plane on estimation accuracy were further investigated with the use of a two-way analysis of variance (ANOVA). The results showed that the estimated joint angles and positions had a good agreement with the reference and the RMSEs below 5.79° and 3.45 cm were respectively achieved. Both the motion type and motion plane had significant effects on the model performance suggesting that the motion task effect should be considered in applications of inertial-based motion analysis methods. The proposed inertial model has great potential in upper limb movement evaluation for patients.