The dynamic properties and non-linear control of the pneumatic muscle actuator (PMA) were investigated in this study for use in a specially designed hand rehabilitation device. The phenomenological model of PMA was established in the lower pressure range applicable for hand rehabilitation. The experimental results show that PMA's characteristics can be approximated by piecewise functions. In order to improve the performance and robustness of control for accurate trajectory tracking, a sliding mode control based on non-linear disturbance observer (SMCBNDO) was designed. The simulation and experimental results demonstrated that the model and the sliding mode control achieved the desired performance in tracking a desired trajectory within guaranteed accuracy. The work indicates that the model and the non-linear control proposed in this study can be applied in PMA-driven hand function rehabilitation devices requiring lower pressures.

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The pseudocapacitive conversion mechanism and robust perovskite fluorides/graphene hetero-nanostructures contribute to fast rate capability, high specific capacity and superior stability.

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Epidural spinal cord stimulation (ESCS) combined with partial weight-bearing therapy (PWBT) has been shown to facilitate recovery of functional walking for individuals after spinal cord injury (SCI). The investigation of neural mechanisms of recovery from SCI under this treatment has been conducted broadly in rodent models, yet a suitable ESCS system is still unavailable. This paper describes a practical, programmable, and fully implantable stimulator for laboratory research on rats to explore fundamental neurophysiological principles for functional recovery after SCI. The ESCS system is composed of a personal digital assistant (PDA), an external controller, an implantable pulse generator (IPG), lead extension, and stimulating electrodes. The stimulation parameters can be programmed and adjusted through a graphical user interface on the PDA. The external controller is placed on the rat back and communicates with the PDA via radio-frequency (RF) telemetry. An RF carrier from the class-E power amplifier in the external controller provides both data and power for the IPG through an inductive link. The IPG is built around a microcontroller unit to generate voltage-regulated pulses delivered to the bipolar electrode for ESCS in rats. The encapsulated IPG measures 22 mm × 23 mm × 7 mm with a mass of  ∼  3.78 g. This fully implantable batteryless stimulator provided a simplified and efficient method to carry out chronic experiments in untethered animals for medical electro-neurological research.

A new robust fractional-order sliding mode controller (FOSMC) is proposed for the position control of a permanent magnet synchronous motor (PMSM). The sliding mode controller (SMC), which is insensitive to uncertainties and load disturbances, is studied widely in the application of PMSM drive. In the existing SMC method, the sliding surface is usually designed based on the integer-order integration or differentiation of the state variables, while in this proposed robust FOSMC algorithm, the sliding surface is designed based on the fractional-order calculus of the state variables. In fact, the conventional SMC method can be seen as a special case of the proposed FOSMC method. The performance and robustness of the proposed method are analyzed and tested for nonlinear load torque disturbances, and simulation results show that the proposed algorithm is more robust and effective than the conventional SMC method.

Estimation of on-off timing of human skeletal muscles during movement is an ongoing issue in surface electromyography (sEMG) signal processing for relevant clinical applications. Widely used single threshold methods still rely on the experience of the operator to manually establish a threshold level. In this paper, a novel approach to address this issue is presented. Based on the generalized likelihood ratio test, the maximum likelihood (ML) method is improved with an adaptive threshold technique based on the signal-to-noise ratio (SNR) estimate in the initial time before accurate sEMG analyses. The dependence of optimal threshold on SNR is determined by minimizing the onset/offset estimate error on a large set of simulated signals with well-known signal parameters. Accuracy and precision of the algorithm were assessed by using a set of simulated signals and real sEMG signals recorded from two healthy subjects during elbow flexion-extension movements with and without workload. Comparison with traditional algorithms shows that with a moderate increase in the computational effort the ML algorithm performs well even for low levels of EMG activity, while the proposed adaptive method is most robust with respect to variations in SNRs. Also, we discuss the results of analyzing the sEMG recordings from the selected proximal muscles of the upper limb in two hemiparetic subjects. The detection algorithm is automatic and user-independent, managing the detection of both onset and offset activation, and is applicable in presence of noise allowing use by skilled and unskilled operators alike.