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.

Early and accurate detection of scalp hair loss is imperative to provide timely and effective treatment plans to halt further progression and save medical costs. Many techniques have been developed leveraging deep learning to automate the hair loss detection process. However, the accuracy and robustness of assessing hair loss severity still remain a challenge and barrier for transitioning such a technique into practice. The presented work proposes an efficient and accurate algorithm to classify hair follicles and estimate hair loss severity, which was implemented and validated using a multitask deep learning method via a Mask R-CNN framework. A microscopic image of the scalp was resized, augmented, then processed through pre-trained ResNet models for feature extraction. The key features considered in this study concerning hair loss severity include the number of hair follicles, the thickness of the hair, and the number of hairs in each hair follicle. Based on these key features, labeling of hair follicles (healthy, normal, and severe) were performed on the images collected from 10 men in varying stages of hair loss. More specifically, Mask R-CNN was applied for instance segmentation of the hair follicle region and to classify the hair follicle state into three categories, following the labeling convention (healthy, normal and severe). Based on the state of each hair follicle captured from a single image, an estimation of hair loss severity was determined for that particular region of the scalp, namely local hair loss severity index (P), and by combining P of multiple images taken and processed from different parts of the scalp, we constructed the hair loss severity estimation ($P_{avg}$) and visualized in a heatmap to illustrate the overall hair loss type and condition. The proposed hair follicle classification and hair loss severity estimation using Mask R-CNN demonstrated a more efficient and accurate algorithm compared to other methods previously used, enhancing the classification accuracy by 4 to 15%. This performance supports its potential for use in clinical settings to enhance the accuracy and efficiency of current hair loss diagnosis and prognosis techniques.

While resistance training promotes muscle hypertrophy and strength, accessibility of equipment is a barrier. This study evaluated a wearable VAriable Resistance Suit (VARS) as a novel and alternative method to achieve muscle strength improvement. It was hypothesized that by providing adjustable, bi-directional and speed dependent resistance, VARS can target specific muscles to improve muscle strength via an accessible and portable device. Twelve untrained healthy male adults (22.08 ± 4.1 years old) participated in an 8-week long resistance training using VARS to strengthen four muscles (biceps brachii, triceps brachii, biceps femoris, rectus femoris) of their non-dominant arm and leg using VARS. The results showed significant improvements in the muscle strength measured by isokinetic dynamometer – 49.9±9.6% increase in isokinetic force and 30.6±7.6% increase in isometric force. Muscle size and body composition were also assessed using ultrasound imaging and bioelectrical impedance analysis, which did not show significant changes. The study demonstrates the efficacy and feasibility of VARS as a resistance training tool to achieve muscle strength improvement and its potential extension to clinical populations.

Convolutional neural networks (CNNs) have been widely used to estimate neural drive from high-density surface electromyography (HD-sEMG) signals in neural machine interfaces owing to their real-time capability. Depending on kernel dimensionality (1D, 2D, or 3D), CNNs can extract temporal, spatial, or spatiotemporal features. Given that motor unit action potentials propagate across both space and time, architectures that exploit spatial features may offer advantages for neural drive estimation. Despite the potential importance of kernel dimensionality, its influence on neural drive estimation remains poorly understood. Existing studies have mainly evaluated CNN generalizability across participants, contraction intensities, or muscles within the same HD-sEMG dataset, while computational efficiency has seldom been considered. As a result, it remains unclear whether different kernel dimensionalities affect cross-dataset generalizability and computational efficiency. In this study, we implemented three CNN architectures — differing only in kernel dimensionality — to investigate whether exploiting the spatial and spatiotemporal features of motor unit action potentials improves the generalizability and computational efficiency of neural drive estimation from HD-sEMG recorded during lower-limb isometric contractions. We trained the CNNs on one HD-sEMG dataset and evaluated them, without retraining, on two independent, unseen datasets recorded from different participants, sessions, and protocols — one spanning three contraction intensities and the other three muscles. All three architectures are generalized to both unseen datasets. The 2D and 3D CNNs marginally outperformed the 1D CNN with a 0.2% increase in R, while the 3D CNN showed no advantage over the 2D CNN. Computational efficiency depended on kernel dimensionality in a platform-dependent manner. On the CPU, the 3D CNN showed the slowest inference time, which was 2× slower than the 2D and 1D CNN, owing to the higher arithmetic cost of its spatiotemporal convolutions. On the GPU, all three architectures achieved similar inference times of about1.36 ms/sample. These findings indicate that increased architectural complexity of CNN does not improve generalizability for neural drive estimation, and that a 2D CNN offers the best balance of accuracy and efficiency for a reliable, deployable CNN-based neural drive estimator—particularly on CPU-only or resource-constrained platforms.

Motor unit (MU) decomposition using high-density surface electromyography (HD-sEMG) has been widely used to characterize MU behavior in neurophysiology and to build neural-machine interfaces for wearable robots. Recently, many open-source software tools for MU decomposition have been made available on GitHub, which could reduce the effort of researchers in the field. However, the consistency among these open-source tools has never been studied, making researchers hesitate to use them. In this study, we collected 7 open-source software tools on GitHub and applied them to decompose MUs from an open-source HD-sEMG dataset (including 11 isometric contraction trials) to investigate the consistency among these tools. To create a comprehensive MU pool for reference, we combined all unique MUs identified by seven tools, visually inspected and removed bad MUs, and manually edited all remaining MU spike trains. Across 7 tools for 11 trials, the number of identified MUs ranges from 167 to 736. The number of valid MUs after expert inspection ranges from 29 to 210, which is 10% to 72% of the reference pool. The rate of agreement between the raw MUSTs and the manually edited MUSTs ranges from 0.86 to 0.94, and the averaged number of edits per MU to correct misalignments ranges from 14 to 39. The results show inconsistency in the implementation and procedures of each tool, which results in an inconsistent number of identified MUs and valid MUs (29 vs 210). In general, a substantial amount of effort is required to process the raw MUSTs from each tool to conduct further research analysis. This study provided a guideline for using open-source software tools for MU decomposition and indicated that it would be beneficial to develop tools to automatically edit the MUSTs.