In recent years, myoelectric control systems have emerged for upper limb wearable robotic exoskeletons to provide movement assistance and/or to restore motor functions in people with motor disabilities and to augment human performance in able-bodied individuals. In myoelectric control, electromyographic (EMG) signals from muscles are utilized to implement control strategies in exoskeletons and exosuits, improving adaptability and human–robot interactions during various motion tasks. This paper reviews the state-of-the-art myoelectric control systems designed for upper-limb wearable robotic exoskeletons and exosuits, and highlights the key focus areas for future research directions. Here, different modalities of existing myoelectric control systems were described in detail, and their advantages and disadvantages were summarized. Furthermore, key design aspects (i.e., supported degrees of freedom, portability, and intended application scenario) and the type of experiments conducted to validate the efficacy of the proposed myoelectric controllers were also discussed. Finally, the challenges and limitations of current myoelectric control systems were analyzed, and future research directions were suggested.

In this paper, a method is considered to observe the stator inter-turn fault (SITF) in brushless DC motors (BLDCM). This is a crucial subject to deal with, since the fault may cause expensive replacement of parts in case of late diagnosis. The approach used here is the discrete wavelet transform, which is one of the many kinds of time-frequency analysis approaches. Taking advantage of a model closely related to a real BLDCM, the stator current is simulated and the aforesaid signal-based method is applied to it. The feature used as a parameter to determine whether SITF has occurred or not is the average change or deviation in the energy amount of four signals named high frequency (detail) signals. In other words, first, the difference percentage of the energy parameters between those four signals in healthy and faulty operation mode would be measured. Then, the average percentage of the energy variation amounts are to be compared with a threshold to determine the fault occurrence. Having a precise Simulink/Matlab plus ADAMS model of the motor, the designed algorithm will be validated.

This research aims to investigate a fault detection method applicable to the stator part of the Brush-Less DC motor (BLDC). Indeed, it is a concern to make sure the motor is operating in a healthy mode, and in any other case, it is of great importance to detect the fault as soon as possible to prevent the further ruin of the major system. Regarding this, a sub-branch method of the Wavelet Transform analysis, named Continuous Wavelet Transform (CWT), is utilized to observe the short-circuit fault in the stator coils. Thus, a novel simulator of the BLDC motor is developed by making an interconnection between ADAMS and MATLAB in which different electrical and mechanical components are included. Therefore, a close-to-reality model of the BLDC motor is achieved, leading to a more accurate evaluation of the proposed method. An energy-type feature will be suggested to characterize the fault happening. Through acquiring the normalized energy amount for one of the wavelet coefficient signals, obtained by the CWT, and comparing the energy with a predefined threshold amount of energy for that signal, it is feasible to detect the stator's flawed performance. By conducting different simulations, the proposed method will be validated.

Muscle disorders and induced muscle atrophy impose critical risks to the well-being of an individual, limiting normal activities of daily living. Several resistance training methods exist that have effectively reversed the progression of muscle atrophy. Weightlifting and hydrotherapy are the two widely practiced schemes for resistance training; however, there is the potential risk of excessive loads exerted on the muscles during weightlifting, and limited accessibility and cost are barriers to hydrotherapy. An alternative is using a resistance band. Some limitations include engaging multiple muscles/joints while only unidirectional resistance is feasible. To address these limitations, a VAriable Resistance Suit (VARS) is designed to provide speed-dependent, bi-directional, and variable resistance at a single joint. As a proof of concept, an elbow module of VARS is developed and validated experimentally. A pilot study shows the changes in flexor and extensor muscle activations in response to eight different levels of resistance modulated by VARS. The clinical implications of VARS remain as future work, but the functionalities implemented in the presented VARS prototype and preliminary results suggest that VARS could be a viable solution and complementary to existing tools and techniques utilized in resistance training.

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