Search Results (2,083)

Motor recovery after stroke, spinal cord injury, or traumatic brain injury reflects relearning rather than simple restitution, as surviving circuits retain plastic potential that can be re-engaged through temporally precise stimulation. This review synthesizes convergent findings demonstrating that Hebbian and spike-timing-dependent mechanisms govern reorganization across cortical, striatal, and spinal levels. Leveraging these timing rules to shape excitability during receptive network states enables durable changes in connectivity and behavior. This effect depends on temporal precision, physiological state, and reinforcement—not stimulus intensity alone—within plasticity windows regulated by metaplastic mechanisms that determine whether Hebbian processes are expressed. Together, these principles define a translational framework for neurorehabilitation, emphasizing biomarker-guided, adaptive, and scalable strategies aligned with intrinsic rules of experience-dependent reorganization. Full article

Background/Objectives: Peripheral vestibular hypofunction (PVH) commonly causes dizziness, imbalance, gait disturbances, and reduced quality of life. Task-oriented circuit training (TOCT) is a rehabilitation approach in which patients perform structured, task-specific functional movements repetitively to improve real-life motor performance. TOCT integrates functional, multisensory, and repetitive exercises based on motor learning and neuroplasticity principles, potentially enhancing rehabilitation outcomes. This study aimed to investigate the effects of TOCT on dizziness, vertigo, balance, gait, disability, and quality of life in patients with PVH. Methods: In this single-blind, randomized controlled trial, 28 patients with PVH were randomly allocated to either a task-oriented circuit training (TOCT) group (n = 16) or a control group (n = 12). The control group performed a conventional home-based vestibular exercise program consisting of gaze stabilization and walking exercises. The TOCT group completed 25 task-specific stations, targeting gaze stabilization, balance, and gait, three times per week for four weeks. Outcomes were assessed at baseline and post-intervention using the Visual Analog Scale for dizziness and vertigo, the Sensory Organization Test for balance, spatiotemporal gait analysis, and the Dizziness Handicap Inventory (DHI) for disability and quality of life. Data were analyzed using two-way repeated-measures ANOVA, with the group × time interaction used to determine whether changes over time differed between the TOCT and control groups. Results: Significant time × group interactions favored TOCT for dizziness severity, vertigo severity, vestibular-related balance parameters, cadence during eyes-closed walking, and DHI total scores (p < 0.05). Within-group analyses demonstrated moderate-to-large improvements in all measured outcomes for the TOCT group, whereas the control group showed limited improvements in dizziness measures and minimal changes in balance, gait, and DHI scores. Conclusions: Task-oriented circuit training significantly improves dizziness, vertigo, balance, gait, disability, and overall quality of life in patients with PVH compared with conventional home-based vestibular exercises. Incorporating functional, multisensory, and task-specific activities within structured circuits may optimize vestibular rehabilitation outcomes. Full article

Muscle onset detection is a fundamental problem in electromyography signal analysis, human–machine interaction, and rehabilitation assessment. In medical and biomedical applications, slow muscle activation onset processes are widely encountered in scenarios such as rehabilitation training, postural regulation, and fine motor control. Such processes are typically characterized by slowly varying amplitudes, long temporal durations, and high susceptibility to noise interference, which poses significant challenges for accurate identification of onset timing. To address these issues, a lightweight temporal attention method for slow muscle activation onset detection is proposed and systematically validated under multimodal experimental settings. The proposed method takes surface electromyography signals as the primary input, while synchronously acquired optical motion image data are incorporated into the experimental design and result analysis, thereby aligning with the common joint use of optical imaging and physiological signals in medical and biomedical research. From a methodological perspective, the proposed framework is composed of lightweight temporal feature encoding, a slow activation-aware temporal attention mechanism, and noise suppression with stable decision strategies. Under the constraint of low computational complexity, the ability to model progressive activation signals is effectively enhanced. Experiments are conducted on a dataset containing multiple types of slow activation movements, and model performance is evaluated using five-fold cross-validation. The results demonstrate that under regular signal-to-noise ratio conditions, the proposed method significantly outperforms traditional threshold-based approaches, classical machine learning models, and several deep learning baselines in terms of onset detection accuracy, recall, and precision. Specifically, onset detection accuracy reaches approximately $92\%$, recall is around $90\%$, and precision is approximately $93\%$. Meanwhile, the average onset detection error and detection delay are reduced to about $41\mathrm{ms}$ and $28\mathrm{ms}$, respectively, with the false positive rate controlled at approximately $2.2\%$. Stable performance is further maintained under different noise levels and cross-subject settings, indicating strong robustness and generalization capability. Full article

Online temperature estimation of key components (windings and magnets) in permanent magnet synchronous motors (PMSMs) has emerged as a critical technology for ensuring the safe operation of PMSMs, preventing insulation degradation, and avoiding the demagnetization of magnets. Because of such advantages, online temperature estimation is attracting growing attention from fields with stringent reliability requirements, such as electric vehicles, as well as electrified railway transportation and more/all-electric aircraft, where similar high-reliability demands exist. This paper gives a comprehensive review of the latest and most effective solutions in the online temperature estimation methods for PMSMs. It analyzes the principles, application progress, and limitations of existing methods, including electrical model-based approaches, thermal model-based approaches, and data-driven approaches, in which process the advantages and challenges of different methods are compared. And an outlook on the future application of this technology are summarized. Full article

Automated video-based detection of cognitive disorders can enable a scalable non-invasive health monitoring. However, existing methods focus on a single disease and provide limited interpretability, whereas real-world videos often contain co-occurring conditions. We propose a novel unified multi-task method to detect depression and Parkinson’s disease (PD) from in-the-wild video data called DEPART (DEpression and PArkinson’s Recognition Technique). It performs body region extraction, Contrastive Language-Image Pre-training (CLIP)-based visual encoding, Transformer-based temporal modeling, and prototype-aware classification with a gated fusion technique. Gradient-based attention maps are used to visualize task-specific regions that drive predictions. Experiments on the In-the-Wild Speech Medical (WSM) corpus demonstrate competitive performance: the multi-task model achieves Recall of 82.39% for depression and 78.20% for PD, compared with 87.76% and 78.20%, for the best single-task models. The multi-task learning initially increases false positives for healthy persons in the PD subset, mainly due to annotation–modality mismatches, static visual content misinterpreted as motor impairments, and occasional body detection failures. After cleaning the test data, Recall for healthy individuals becomes comparable across models; the multi-task model improves Recall for both depression (from 82.39% to 87.50%) and PD (from 78.20% to 86.14%), suggesting better robustness for real-life clinical applications. Full article

Anticipation allows individuals to prepare actions by predicting upcoming events, yet its influence on motor learning and its practical relevance for rehabilitation remain unclear. This study investigates how anticipation mechanisms shape motor learning and skill acquisition in a virtual leader–follower task and explores their potential for adaptive training. Forty-nine healthy adults performed a joystick-controlled tracking task in virtual reality, following a dynamic leader that was always visible (Control), became invisible at regular intervals (Deterministic Anticipation), or disappeared randomly (Stochastic Anticipation) to elicit anticipatory behavior. Kinematic and kinetic metrics and time-series analysis were used to evaluate synchrony, smoothness, and coordination. Performance improved from baseline to retention, with no distinct differences in final performance between the groups. However, slope-based analyses found that anticipation-based training accelerated learning, especially in the novice subgroup (baseline score < 35), with marked improvements in metrics such as score pause duration, temporal lag, and spatial error. Although participants reached similar final performance levels across protocols, the rate and pattern of learning differed across training protocols. Anticipation accelerates early-stage improvements, with the strongest effects observed in novice participants. The paradigm provides a high-resolution framework for adaptive motor training and assessment. Full article

The aim of this research is to investigate and reduce the torque ripple in Switched Reluctance Motor (SRM) drives, which is one of the major barriers to their acceptance for electric vehicle propulsion applications despite the advantages of robustness, efficiency, and wide operating range. High torque ripple not only deteriorates drive smoothness but also contributes to noise and vibration, demanding an advanced control strategy beyond traditional current-shaping and switching-based approaches. In this context, this work proposes a DDPG (Deep Deterministic Policy Gradient) Actor–Critic Neural Network-based reinforcement learning control framework that learns the optimal firing angle offsets dynamically to ensure less ripple electromagnetic torque under varying speeds and load conditions. The developed strategy has been designed and trained in MATLAB Simulink R2024b and then deployed in real time using an FPGA-based digital controller for validation on hardware. Comparative analysis with TSF (Torque Sharing Function) and DITC (Direct Instantaneous Torque Control) demonstrates that the reinforcement learning approach gives a much smoother torque response with better dynamic behavior over the operating range analyzed. Full article

Human movement emerges from complex interactions between neural processes, musculoskeletal dynamics, and environmental constraints, resulting in behavior that is inherently nonlinear. Therefore, nonlinear dynamical systems approaches have been widely used to characterize variability, stability, and coordination in motor behavior. However, despite their conceptual value, these methods are often applied post hoc and remain limited in their ability to support prediction, control, and integration of high-dimensional multimodal data. Artificial intelligence (AI) provides a complementary modeling framework capable of addressing these limitations. Yet many current AI applications treat motor signals primarily as feature sets for classification or regression, leaving the underlying dynamical structure of movement underexplored. This review synthesizes recent research that integrates AI with nonlinear motor control analysis to model, interpret, and control human movement across neural, biomechanical, and behavioral domains. We organize related studies according to the type of nonlinear motor control problem addressed, including input–output mappings, temporal dynamics, and adaptive control policies under conditions of partial observability and nonstationarity. Across these examples, we show that AI becomes scientifically informative when constrained and evaluated by nonlinear dynamical constructs such as attractors, phase relationships, manifolds, and stability structures. Finally, we discuss current limitations and outline future directions toward theory-informed, explainable, and closed-loop AI models for motor control and human–actuator interaction. Full article

Rare Earth Elements (REEs) represent strategic and critical raw materials for the energy transition and must therefore be integrated into efficient and functional recycling processes. Their adoption in electric motors is rapidly expanding, raising significant challenges for end-of-life (EoL) management, starting from the collection phase. In this context, this work proposes the integration of an image-based classification framework within the Waste Electrical and Electronic Equipment (WEEE) recycling pipeline to selectively identify electric motors containing permanent magnets (PMs) and direct them toward dedicated recycling processes for rare earth recovery. The proposed methodology relies on a Discriminative Transfer Learning (DTL) approach based on a ResNeXt convolutional neural network (CNN), adapted to a proprietary and heterogeneous dataset of electric motors acquired in an industrial recycling facility. The objective is twofold: first, to identify motors containing PMs; second, to classify motors into construction categories according to their likelihood of incorporating PMs. Experimental results show promising performance in terms of PM-containing motor detection capability, establishing a robust foundation for the automated recovery of REEs at an industrial scale. Furthermore, the model’s generalization capabilities can be further enhanced through the expansion of collaborative datasets and the integration of advanced scanning technologies. Full article

This investigation assessed the neurovascular coupling response through integrated assessments of neuronal function [electroencephalography (EEG)], microvascular oxygenation concentrations [functional near-infrared spectroscopy (fNIRS)], and arterial responses [transcranial Doppler ultrasound (TCD)]. The NVC response was assessed in 113 participants (86 females, aged 19–40 years) during visual (“Where’s Waldo?”) and motor (finger tapping) tasks. Block-averaged, time–frequency power was computed from the EEG data, while hemodynamic response functions were obtained from the fNIRS and TCD metrics. Granger causality assessed the predictiveness between EEG-fNIRS-TCD waveforms for each participant and was converted into a percentage of individuals displaying a significant value. Linear models were computed to determine the influence of sex, concussion history, young adulthood age, cardiorespiratory fitness, and mental health/learning disabilities on NVC parameters. During the initial 10 s of task onset, unidirectional predictiveness was weak to very strong for EEG-TCD (range: 47–83%) and fNIRS-TCD (44–92%) relationships; however, very weak to weak predictiveness was seen for the E0EG-fNIRS (0–29%) relationship for both tasks. Aside from known sex-, age-, and fitness-based influences on baseline/peak hemodynamic values (p < 0.050), the addition of concussion history and mental health/learning disabilities had minimal influence on NVC responses (p > 0.050). The findings demonstrated a unidirectional feedforward mechanism from the neuronal and microvasculature to the upstream arteries during task onset. Full article

STT3A encodes the catalytic subunit of the oligosaccharyltransferase A (OST-A) complex and is classically linked to severe autosomal-recessive congenital disorder of glycosylation (CDG). To define the distinct autosomal-dominant disorder, we reviewed all published cases and integrated three previously unpublished individuals from the CDG natural history study. Across 21 individuals, abnormal transferrin glycosylation was present in nearly all individuals (20/21), and subtle facial dysmorphism was common (18/21). Neurodevelopmental involvement was frequent, including motor delay (13/21), learning difficulties (13/21), speech delay (12/21), and intellectual disability (10/21). Musculoskeletal manifestations were also common, including skeletal abnormalities (12/21), short stature (11/21), muscle cramps (8/21), and early-onset osteoarthritis in adults (6/21). Less frequent features included congenital heart defects (5/21) and coagulation factor deficiency (5/21). Importantly, the newly reported individuals expand dominant STT3A-CDG with previously unreported features, including anorectal malformation, morbid obesity, and clinically significant bleeding diathesis with von Willebrand factor and factor VIII deficiency. Biochemical signatures ranged from classic type I transferrin patterns to subtle or atypical abnormalities, emphasizing that near-normal transferrin testing does not exclude the diagnosis. Variants clustered in conserved catalytic regions, with recurrent p.Arg405 across de novo, inherited, and mosaic cases supporting a mutational hotspot and likely dominant-negative mechanism. Full article

Brain–computer interfaces (BCIs) have great potential for consumer electronics, as they enable the decoding of brain activity to control external devices and assist human–computer interaction. However, current decoding methods for BCIs face several challenges, such as low accuracy, poor stability under electrode shift, and slow processing for real-time use. In this paper, we propose a hybrid decoding framework designed to address the challenges of current EEG decoding methods. Our method combines manifold learning with contrastive learning. The core of our method lies in a dual-manifold model that uses non-negative matrix factorization (NMF) and a contrastive manifold learning framework to extract clear and useful features from brain signals. To improve decoding stability, we introduce a joint training strategy that enhances feature learning. Furthermore, the system is optimized for real-time interaction, reducing the system latency to 100 ms. We collect EEG signals from 15 subjects performing motor execution tasks and 10 subjects performing motor imagery tasks to construct a motor EEG dataset. On this dataset, the proposed method achieves superior decoding performance, reaching F1-scores of 0.7382 for the motor imagery tasks and 0.8361 for the motor execution tasks. Furthermore, the method maintains robustness even with reduced electrode counts and altered spatial distributions, highlighting its potential as a decoding solution for reliable and portable BCI systems. Full article

This paper presents a unified framework for reliable motion control of permanent magnet linear motors (PMLMs) by integrating fault detection (FD) and fault-tolerant control (FTC). The framework combines a brain emotional learning-based intelligent controller (BELBIC) with a linear extended state observer (LESO) to enable rapid detection and mitigation of abrupt and incipient faults, as well as disturbances and sensor noise that degrade tracking accuracy and system reliability. The LESO is employed to estimate unknown dynamics and lumped disturbances and to generate residuals for reliable fault detection, while BELBIC provides adaptive and robust control actions without requiring prior knowledge of system parameters or explicit fault models. Extensive simulation studies under actuator faults, system dynamics faults, external disturbances, and measurement noise are conducted. Comparative evaluations with benchmark approaches demonstrate improved fault detection speed, tracking accuracy, and robustness of the proposed framework, highlighting its potential for enhancing reliability and operational continuity in high-precision industrial applications. Full article

We classified essential motor actions, dorsal and plantar flexion (lower limb), and arm movement (upper limb) from electroencephalography (EEG)-based brain–computer interface (BCI) signals, using a convolutional neural network (CNN). Different from previous research on upper or lower limb motor imagery in isolation, we integrated both categories in a unified framework to explore a broader range of movements for broader applications. These motor actions are fundamental to daily activities such as walking, running, maintaining balance, lifting, reaching, and exercising. Upper limb EEG data were provided by INTI International University, whereas lower limb data were obtained from a publicly available dataset, recorded using 16-channel Emotiv and OpenBCI systems, respectively, each with distinct sampling rates and signal formats. To improve signal quality and facilitate joint model training, all signals were downsampled to 125 Hz, standardized to 16 channels, segmented using sliding windows, normalized via StandardScaler, and labelled according to action class. The processed data were used to train a CNN model configured with a kernel size of 3 and rectified linear unit activation functions. Training was terminated early at epoch 11 using an early stopping strategy, resulting in approximately 67% accuracy for both training and validation sets. Although this accuracy was moderate for deep learning, a promising outcome for EEG-based multi-class motor imagery classification was obtained, with the challenges posed by limited data availability, low inter-class feature discriminability, and the inherently noisy nature of non-invasive EEG signals. The results of this study underscore the potential of CNN-based models for future real-time BCI applications. By expanding the dataset, deep learning architectures can be refined to improve signal preprocessing techniques. Prosthetic devices need to be integrated to validate the system in practical scenarios. Full article

This paper proposes a reinforcement learning (RL)-based control method for robust path tracking of a 4-wheel independent steering (4WIS) mobile robot in outdoor rough terrain environments. Traditional wheeled robots typically suffer from limitations including mobility constraints in narrow spaces, path deviations caused by ground slip, and reduced traction on rough terrain. To address these challenges, we designed a 4WIS mobile robot and implemented an architecture that independently controls the steering and driving of each wheel. The RL state space is defined by look-ahead path information, robot pose, velocity, and tracking errors, while the action space consists of target angular velocity and steering angle. To ensure robust performance, we applied random path and terrain generation and implemented domain randomization for sensors and actuators based on empirical GPS and motor data. The proposed controller was validated against the Pure Pursuit algorithm through dynamic simulations and real-world experiments. In simulations mimicking outdoor terrain, the controller reduced lateral and heading RMSE by 6.32% and 16.00%, respectively. In actual outdoor environments, it reduced these errors by 21.54% and 4.78%, respectively. These results demonstrate that the proposed controller provides superior robust tracking performance in unstructured outdoor environments. Full article