Long Meng

Wearable surface electromyography (sEMG)-based gesture recognition enables intuitive human–machine interaction, but practical deployment is often limited by hardware constraints, model complexity, and inter-subject variability. In this study, we developed a high-performance wrist-worn sEMG acquisition system and a lightweight neural network, ThinNet, to achieve efficient and accurate gesture recognition. The wristband features a ring-shaped differential electrode array and embedded filtering modules, achieving a signal-to-noise ratio (SNR) of 66.96 dB, significantly higher than commercial devices. Using data from 100 participants performing six gestures, ThinNet achieved 90.47% inter-subject accuracy, with peak accuracy reaching 96.80% under a three-tier buffered decision strategy. Systematic analysis demonstrated that the model maintains high performance with only 40% fine-tuning data, indicating excellent data efficiency. Importantly, the framework supports scalability across additional users and practical deployment in real-world applications. These results highlight the combined effectiveness of hardware optimization and algorithm design in advancing wearable sEMG-based gesture recognition systems.

Integrating thermoreversibility with electrical conductivity in a unified hydrogel platform enables long-term, reusable through-hair neural interfaces. However, achieving both simultaneously remains challenging, as thermoreversibility demands network reorganization while conductivity necessitates network percolation. Here, we engineer phase morphology by controlling the components’ viscoelastic state during mixing. Ionically conductive nucleated morphologies illustrated by liquid-liquid phase separation exhibit rapid thermoreversibility, whereas electrically conductive bicontinuous phases demonstrated by viscoelastic phase separation achieve a marginal gel-sol transition and an ultralow storage modulus of ~1.7 kilopascals while simultaneously achieving a conductivity of 7.5 siemens per centimeter or transconductance of 5.1 millisiemens in an organic electrochemical transistor. Below this threshold, systems resemble nucleated behavior, whereas above it, superior semiconducting properties emerge, but phase transition capability is lost. These materials enable reusable through-hair neural interfaces to maintain low skin contact impedance of 1.6 kohm·cm ² across different hair types for 3 days, facilitating stable event-related desynchronization detection during mechanical and electrical haptic sensation for personalized haptics.

Functional electrical stimulation (FES) is a prevalent technique commonly used to activate muscles in individuals with neurological disorders. Traditional FES strategies predominantly utilize low-frequency (LF) stimulation, which evokes synchronous action potentials, leading to rapid muscle fatigue. To address these limitations, we introduced a subthreshold high-frequency (HF) stimulation method that employed continuous, charge-balanced subthreshold current pulses at kilohertz frequencies, designed to evoke motor unit (MU) activation similar to voluntary activation. We evaluated the effectiveness of HF stimulation on the reduction of muscle fatigue across different force levels (10%, 25%, and40% of maximum force). The HF stimulation utilized continuous charge-balanced, brief pulses of 80 μs (at a 10 kHz frequency) targeted the ulnar/median nerve bundles. We compared the fatigue effects with conventional LF stimulation and voluntary muscle contractions. Our results indicated that HF stimulation maintained more sustained force outputs and muscle activation over a prolonged time compared with LF stimulation. The HF stimulation also evoked a more dispersed muscle activation pattern, similar to voluntary muscle contractions. These findings suggest that HF stimulation can significantly enhance the sustainability of muscle contractions and reduce muscle fatigue, potentially improving the efficacy and applicability of FES in clinical and home-based settings for individuals with neurological impairments.

Accurate hand movement prediction plays a pivotal role in advancing robotic control technologies. Neuronal firing signals, as the driving representation of motor intentions, offer a physiologically meaningful approach to decode motor commands. These representations are typically extracted using blind source separation techniques. However, the high computational intensity of these methods limits practical applications. Therefore, we directly learned neuronal firing representations from surface electromyogram (sEMG) signals via an efficient deep forest (DF) framework. Specifically, we first obtained populational neuronal firing rate signals as the ground truth. The DF model was trained to map sEMG signals directly to populational neuronal firing rate. To enable robust and continuous finger force predictions, we evaluated the DF framework on data obtained across multiple sessions, with an average session interval of 6.58 days. Our results revealed that the DF framework accurately maps sEMG amplitudes to neuronal firing representations, achieving comparable accuracy to source-separation-based method with significantly reduced computational time. The developed DF model also outperformed neural network models and other decision-tree-based ensemble methods. Furthermore, despite utilizing the same input features, the DF framework significantly outperformed the sEMG-amplitude approach, showcasing its capacity to capture complex neural drive information for more accurate finger force predictions. Moreover, the robustness test against noise interference revealed that the DF framework maintained stable performance under different noise levels. These findings highlight the potential of DF framework as an efficient solution for real-time robotic control applications.

Objective. Accurate prediction of motor unit (MU) discharge activity from surface electromyogram (sEMG) signals is critical for understanding neuromuscular control and for enabling practical neural interface applications. However, current MU decomposition approaches rely on person-specific data, limiting their generalizability. Approach. We developed a cross-person decomposition framework and validated the algorithm using synthesized high-density sEMG data by convoluting simulated MU firing spike trains with action potential templates derived from human experimental data. We first obtained separation matrix from multiple training subjects and applied them to decompose sEMG signals from unseen test subjects. This allowed us to obtain MU spike trains. The predicted outcomes were then compared with the ground truth across multiple metrics, including spike detection accuracy, MU firing rate (FR), waveform similarity of MU action potentials (MUAPs), and MU recruitment thresholds. Main results. Our results demonstrated strong agreement between predicted and true MU activity. Specifically, we found high R 2 values (⩾0.95) for the populational FR, and the coefficient of variation of FR remained stable across different MU retention thresholds. The MU similarity analyzes revealed that the predicted MUAPs closely matched ground truth counterparts both in waveform shape and spatial distribution. Furthermore, recruitment thresholds exhibited strong linear relation ( R 2 = 0.98 ± 0.006) with minimal error. Significance. These findings demonstrate the feasibility of efficient cross-person MU decomposition with minimal accuracy loss, laying the groundwork for generalized, plug-and-play myoelectric systems in neurophysiology, neuroprosthetic, and rehabilitation applications.

As wearable technology and machine learning (ML) algorithms have developed by leaps and bounds, many studies have focused on automatically monitoring Parkinson’s Disease (PD). The procedure includes two steps: firstly, classifying activities, usually the Movement Disorder Society-sponsored revision of the unified Parkinson’s disease rating scale (MDS-UPDRS) part III tasks; secondly, evaluating the performance of these activities and recognizing PD. To improve the efficiency and precision of the procedure, this study proposes a self-attention squeeze-and-excitation temporal convolutional network (SASE-TCN). Based on the multi-stage temporal convolutional network (MS-TCN) and attention mechanisms, SASE-TCN can effectively extract the distant sequential and channel-wise features. In addition, based on acceleration signals, SASE-TCN can classify MDS-UPDRS III tasks and recognize patients with PD simultaneously with the multitask learning mechanism. The model was tested with two public datasets, PD-BioStampRC21 and PD-motion. The SASE-TCN model obtained an average F1 score of 0.7802-0.9160 for activity classification and an average accuracy of 0.7366-0.8578 for PD recognition. The results demonstrated the feasibility of the SASE-TCN in classifying PD-related activities and recognizing PD with accelerometers. This study will support the diagnosis and treatment of PD.

Surface electromyogram (EMG) signals have been a preferred modality for motor intent detections in the fields of robotic control, rehabilitation, and health monitoring. However, current EMG-based measurement techniques suffer a degradation in performance cross session over time due to factors such as shifts in electrode placement, changes in muscle states, and environmental noise. To address this challenge, we developed a novel neural-drive approach, capable of robust cross-day predictions of individual finger forces. Specifically, high-density EMG data were collected from flexor and extensor muscles during single-finger and multi-finger tasks. The experimental procedure was repeated three times (sessions), with an average interval of 6.58 days between sessions. We first decomposed the EMG signals in a session to obtain separation matrices that contained motor unit information in the EMG signals. We then refined the separation matrices that accurately reflected individual fingers. The corresponding separation matrices were applied to EMG signals in the other two sessions to derive the neural drive for force predictions of individual fingers. Our results revealed that the cross-session performance was comparable with the within-session performance. In addition, the neural-drive approach can outperform the conventional EMG-amplitude approach, especially in the cross-session performance. Our developed approach can enhance the long-term reliability of finger force predictions and holds potential for various practical applications.

Neural or muscular injuries, such as due to amputation, spinal cord injury, and stroke, can affect hand functions, profoundly impacting independent living. This has motivated the advancement of cutting-edge assistive robotic hands. However, unintuitive myoelectric control of these devices remains challenging, which limits the clinical translation of these devices. Accordingly, we developed a robust motor-intent decoding approach to continuously predict the intended fingertip forces of single and multiple fingers in real time. We used population motor neuron discharge activities (i.e., neural drive from brain to spinal cord) decoded from a high-density surface electromyogram (HD-sEMG) signals as the control signals instead of the conventional global sEMG features. To enable real-time neural-drive prediction, we employed a convolutional neural network model to establish the mapping from global HD-sEMG features to finger-specific neural-drive signals, which were then employed for continuous and real-time control of three prosthetic fingers (index, middle, and ring). As a result, the neural-drive-based approach can decode the motor intent of single-finger and multifinger forces with significantly lower force estimation errors than that obtained using the global HD-sEMG-amplitude approach. Besides, the force prediction accuracy was consistent over time and demonstrated strong robustness to signal interference. Our network-based decoder can also achieve better finger isolation with minimal forces predicted in unintended fingers. Our work demonstrates that the accurate and robust finger force control could be achieved through this new decoding approach. The outcomes offer an efficient intent prediction approach that allows users to have intuitive control of prosthetic fingertip forces in a dexterous way.