IEEE Transactions on Neural Systems and Rehabilitation Engineering最新文献

This study investigates functional performance using a two-degree-of-freedom (2DOF) prosthetic wrist compared to a single-degree-of-freedom (1DOF) wrist in individuals with transradial (below-elbow) amputation. Five participants were fitted with a custom-designed 2DOF prosthetic wrist system integrated with an Ottobock Transcarpal hand and operated via a pattern recognition-based myoelectric control interface. Participants completed two test conditions: one using wrist rotation alone (1DOF, NoWF), and another using wrist rotation combined with wrist flexion and extension (2DOF, WF). A battery of standardized functional assessments was used to evaluate performance in both conditions, including the Southampton Hand Assessment Procedure (SHAP), Box and Blocks Test (BBT), Jebsen-Taylor Hand Function Test (JTHFT), Activity Measure for Upper Limb Amputees (AM-ULA), Clothespin Relocation Task (CRT), and the Assessment of Capacity for Myoelectric Control (ACMC). Across all outcome measures, no statistically significant differences were found between the 1DOF and 2DOF conditions. While the lack of measurable improvement may reflect the influence of factors inherent to the 2DOF design, such as its greater length, added mass compared to 1DOF wrists, or increased control complexity, the results nonetheless indicate that the addition of a second wrist degree of freedom did not compromise functional performance. These findings suggest that more complex multi-DOF systems can be implemented without detriment to user function, an encouraging result for the continued development of advanced upper-limb prosthetic technologies.

Prosthesis users often experience muscle fatigue and reduced control due to the weight of the device, contributing to high abandonment rates. This study investigates the effects of integrating a soft exoskeleton with a myoelectric prosthesis on upper-limb muscle fatigue and user experience. Nine able-bodied participants performed four functional tasks: drinking from a cup, using a Fork, lifting a box, and reaching overhead, using the prosthesis alone and in combination with the exoskeleton. Muscle activity was recorded via surface electromyography, and perceived exertion was measured using the Borg scale. Kinematics and workload were also assessed through motion capture and the NASA-TLX questionnaire. Usability was evaluated using the System Usability Scale (SUS). Results showed that exoskeleton assistance significantly reduced muscle activation, particularly in the Deltoid, Biceps, and Triceps Lateral Head during the Lift task, with RMS reductions up to 64 % and large effect sizes. Perceived exertion slopes decreased across all tasks, with some instances showing stabilization or reduction during activity. Kinematic analysis indicated minimal impact on shoulder range of motion, with slight adjustments in internal/external rotation remaining within physiological norms. NASA-TLX scores suggested reduced physical demand and effort, and SUS responses indicated moderate usability with room for improvement. These findings demonstrate that soft exoskeletons can effectively unload muscles and reduce fatigue during prosthesis use, highlighting their potential to enhance endurance, task performance, and user comfort. Future work should extend assistance to additional joints and evaluate the system with upper-limb amputees in real-world scenarios.

Accurate and reliable estimation of joint torques in the lower limb is essential for assisted control of rehabilitation exoskeleton robots that help patients recover. Surface electromyography is a valuable tool for revealing human motion. According to the myoelectric signal generation mechanism, muscle mechanical process, and muscle synergy theory, this study developed a model at the neural control level that relates myoelectric signals and lower limb joint torques. First, a muscle bioelectrical activation model based on electromyography signals is established. Second, a motor nerve activation model based on muscle synergy was developed to characterize muscle activation as muscle synergy structure and muscle synergy activation coefficients. Then, a continuous estimation model for lower limb joint torques with multi-scale feature fusion enhancement is established by integrating deep learning methods with the self-attention mechanism. The adversarial transfer learning method is used to optimize the model, enabling it to adapt to long-term use by patients during rehabilitation movements. Experimental results involving eight patients with lower limb motor dysfunction demonstrate that our proposed model can accurately estimate the torques of the hip and knee joints. It maintains strong performance over long-term use, with decision coefficients of 0.92 ± 0.06 for hip joints and 0.95 ± 0.03 for knee joints. This study lays the groundwork for further development of on-demand assisted control for exoskeletons.

Despite advancements in bionic technology, disrupted afferent and efferent pathways in individuals with limb loss may hinder prosthetic embodiment. Transcranial Magnetic Stimulation (TMS) has emerged as a promising tool for investigating and modulating sensorimotor processes, though its application in neurorehabilitation for individuals with limb differences remains limited. This pilot study explores whether TMS over parietal regions associated with the grasping-function and motor planning can enhance the sense of embodiment and influence motor behaviour. In this feasibility protocol, six healthy participants and two prosthesis users underwent a virtual reality TMS-training protocol. Its effects were assessed through pre- and post-training evaluations using standard self-reported embodiment surveys, functional performance metrics, and a Locus of Attention Index. Participants completed two assessments without TMS, before and after a training session with TMS. Additionally, one final assessment with TMS was conducted, designed to evaluate its direct impact on performance metrics. Results in healthy participants indicate that while TMS did not significantly alter the perceived embodiment, it affected the visual attention allocation. Additionally, trajectory velocity differed between no-TMS and TMS assessments. Finally, the online system evaluation on two prosthesis users highlights the feasibility of applying parietal TMS in training for prosthetic embodiment research, warranting further investigation with larger and more comprehensive samples to better establish the role of TMS in sensorimotor rehabilitation with amputees.

At-home rehabilitation for post-stroke patients presents significant challenges, as continuous, personalized care is often limited outside clinical settings. Moreover, the lack of integrated solutions capable of simultaneously monitoring motor recovery and providing intelligent assistance in home environments hampers rehabilitation outcomes. Here, we present a multimodal smart home platform designed for continuous, at-home rehabilitation of post-stroke patients, integrating wearable sensing, ambient monitoring, and adaptive automation. A plantar pressure insole equipped with a machine learning pipeline classifies users into motor recovery stages with up to 94% accuracy, enabling quantitative tracking of walking patterns during daily activities. An optional head-mounted eye-tracking module, together with ambient sensors such as cameras and microphones, supports seamless hands-free control of household devices with an average latency under 1 s with consistent operation. These data streams are fused locally via a hierarchical Internet of Things (IoT) architecture, ensuring low latency and data privacy. An embedded large language model (LLM) agent, Auto-Care, continuously interprets multimodal data to provide real-time interventions-issuing personalized reminders, adjusting environmental conditions, and notifying caregivers. Implemented in a post-stroke context, this integrated smart home platform increased mean user satisfaction from 3.9 ± 0.8 in conventional home environments to 8.4 ± 0.6 with the full system (n = 20). Beyond stroke, the system offers a scalable, patient-centered framework with potential for long-term use in broader neurorehabilitation and aging-in-place applications.

Transcranial AC stimulation (tACS) of the cerebellum can entrain spiking activity in the Purkinje cells (PCs) of the cerebellar cortex and, through their projections, the cells in the cerebellar nuclei (CN). In this paper, we investigated if the cells in the motor thalamus (Mthal) can also be modulated (i.e. spikes entrained) via the CN-Mthal projections in rodents. A total of 82 thalamic cells were found, presumably in the Mthal by their stereotaxic coordinates, that were modulated by tACS of the cerebellum. Out of the 346 cells isolated, the thalamic cells with shorter action potentials and regular firing patterns had a higher probability of modulation by cerebellar stimulation than the cells with wider action potentials. The modulation level had a tuning curve with a maximum around 100-200 Hz. Spike histograms over the stimulation cycle transitioned between unimodal and bimodal distributions depending on the frequency. Most cells had a unimodal distribution at low frequencies, a bimodal distribution for frequencies between 80-125 Hz, and then a unimodal one for frequencies above 150 Hz. In addition, tACS of the motor cortex (MC) was also tested in a subset of thalamic cells. Unlike cerebellar stimulation, modulation levels peaked at two distinct frequencies, presumably due to entrainment through multiple MC-Mthal pathways with different preferred frequencies. The results demonstrate the feasibility of modulating a deep brain structure such as the thalamus through multi-synaptic pathways by stimulation of the cerebellar cortex (and the motor cortex) using a non-invasive neuromodulation method.

Polysomnography (PSG) signals are essential for studying sleep processes and diagnosing sleep disorders. With the advancement of deep neural networks (DNNs), automated analysis of PSG data has become increasingly feasible. However, the limited availability of data for certain sleep events often restricts DNNs to single-task learning on a single-source dataset, limiting their ability to generalize to new events and reducing robustness across datasets. To address these challenges, we propose PSG-MAE, a pretraining framework based on the masked autoencoder (MAE). By leveraging self-supervised learning on large volumes of unlabeled PSG data, PSG-MAE trains a robust feature extraction network applicable to diverse sleep event monitoring tasks. Unlike conventional MAEs, PSG-MAE applies complementary masking across PSG channels, integrates a multichannel signal reconstruction mechanism, and incorporates an inter-channel contrastive learning (ICCL) strategy. This design enables the encoder to capture temporal features from each channel while simultaneously modeling latent inter-channel relationships, thereby enhancing the utilization of multichannel information. Experimental results demonstrate that PSG-MAE effectively learns both temporal details and inter-channel dependencies from PSG signals. When the pretrained encoder is fine-tuned with downstream sleep event monitoring networks, it achieves a macro-averaged F1-score of 81.0% for sleep staging and 82.6% for apnea detection on the SHHS dataset. Cross-dataset validation on the MESA dataset further confirms the framework's robustness and broad applicability. The code of PSG-MAE is available at https://github.com/yfw-scut/PSG-MAE.git.

With the rapid growth of the elderly population, fall accidents have received increasing attention due to their serious health hazards. Pre-impact fall detection (PIFD) based on wearable sensors emerges as a promising approach for proactive fall prevention in healthcare monitoring. In this research, based on Inertial Measurement Units (IMUs), we construct and publicly provide a large-scale motion dataset named FallTL, which includes falls and activities of daily living (ADLs) collected from multiple body segments. Furthermore, we develop STA-Net, a novel Spatial-Temporal Attention Network to perform PIFD based on IMU data from a single body segment. STA-Net incorporates a dual-branch architecture: a temporal attention branch that models temporal signal dependencies and a spatial attention branch that captures cross-modality feature interactions, enabling robust representation learning from sensor data. We evaluate STA-Net across three datasets and it achieves advantageous performance and comparable lead time under cross-subject validation, outperforming state-of-the-art baselines. In addition, our analysis further investigates the influence of sensor placement and data modality on detection performance. These results indicate that accurate and robust PIFD is feasible with minimally obtrusive, single-location sensor setups, offering practical implications for wearable fall monitoring systems.

In recent years, motion-capture technologies have been widely applied in rehabilitation to enhance patient engagement and therapeutic efficacy. Facial paralysis, characterized by impaired facial muscle function due to facial nerve injury, is typically managed through pharmacotherapy and electroacupuncture. However, physical therapy, such as facial muscle exercises, is pivotal in promoting neural remodeling. In this pilot study, we developed an Adaptive Gamified Facial Rehabilitation System based on real-time facial tracking: patients control an avatar by contracting specific facial muscles to fire bullets, and the game automatically adjusts its difficulty according to each user's performance. We recruited ten healthy volunteers and one patient with unilateral facial paralysis. The patient conducted a 21-day intervention combining conventional care with the game-based training system, focusing on activating the zygomaticus major muscle to mitigate drooling and restore facial symmetry. Surface electromyography (EMG) was recorded to quantify muscle activation, and weekly assessments of facial muscle function were conducted. Following the intervention, muscle activation on the affected side increased to 81-90% of the healthy baseline, the House-Brackmann grade improved from IV to II, and Facial Disability Index (FDI) scores rose significantly. Moreover, EMG metrics approached those of the control group. These findings demonstrate that our Adaptive Gamified Facial Rehabilitation System can substantially augment neuromuscular rehabilitation in patients with facial paralysis, offering a feasible approach for personalized neurorehabilitation engineering.