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.

This paper presents a new method for user localization in single-site massive Multiple-Input-Multiple-Output (MIMO) systems, which circumvents the need for large labeled datasets typically required for training data-driven models. Instead, the proposed model utilizes a limited set of geo-tagged Channel State Information (CSI) samples for training. The approach combines a Fully-Connected Auto-Encoder (FC-AE) with a Gaussian Process Regression (GPR) model. The GPR model is efficient, as it requires only a minimal amount of labeled data for training, although it presents challenges in computational complexity. To address this complexity, the FC-AE is introduced, which encodes the Angle-Delay Profile (ADP) transformation of the CSI data. The training dataset for the FC-AE is crafted by employing data augmentation techniques on a small collection of unlabeled data. The simulation results demonstrate that FC-AE is scenario-independent and adaptable to new scenarios with similar ADP characteristics. Additionally, our FC-AE-GPR model surpasses the performance of the Convolutional Neural Network model and the non-parametric grid search method when provided with limited labeled data, applicable in both indoor and outdoor settings.