Ping Shi

Current dysphagia assessment suffers from subjectivity and reliance on complex clinical tools, while rehabilitation lacks objective feedback. The critical mechanism of respiration–swallowing coordination (RSC) for preventing aspiration is often overlooked in existing monitoring systems. This study aimed to develop and validate an intelligent integrated system for dysphagia rehabilitation and that provides a closed-loop solution for quantitative assessment and personalized rehabilitation. The system’s core innovation is the tight integration of a wireless wearable hardware subsystem for synchronous swallowing sound and respiratory waveform acquisition with an intelligent software subsystem. The software incorporates novel algorithms for automated swallowing identification, respiratory cycle analysis, and real-time classification of RSC patterns into aspiration risk levels. This enables an interactive platform that closes the loop between assessment, visual feedback, and personalized audiovisual guidance for rehabilitation training. Validation included hardware performance tests and a clinical trial with 16 participants (8 healthy participants and 8 participants with dysphagia). The hardware demonstrated reliable performance, achieving a robust swallowing sound SNR (14.07 dB in a noisy environment) and showing acceptable temporal consistency in the respiratory waveform. On a 0–100 scale, the system significantly differentiated swallowing function between healthy (82.72) and dysphagic (57.25) subjects ( p < 0.01). We also conducted an exploratory experiment in two dysphagic participants using audiovisual guided training. A preliminary upward trend in swallowing-coordination scores was observed after training, suggesting a potential short-term training effect of the audiovisual guidance module. The integrated system represents an innovation by seamlessly merging wearable sensing with intelligent, coordination-focused algorithms into a unified closed-loop platform. It offers a practical and effective solution for transforming dysphagia management towards objective, personalized, and intelligent rehabilitation.

Objective: Cardiac monitoring in community and home environments requires automated operation, cross-state robustness, and interpretable feedback under resource-constrained and uncontrolled conditions. Unlike accuracy-driven ECG–PCG studies focusing on diagnostic performance, this work emphasizes systematic modeling of cardiac electromechanical coupling for long-term monitoring and engineering feasibility validation. Methods: An automated ECG–PCG coupling analysis and semantic reporting framework is proposed, covering signal preprocessing, event detection and calibration, multimodal coupling feature construction, and rule-constrained LLM-assisted interpretation. Electrical events from ECG are used as global temporal references, while multi-stage consistency correction mechanisms are introduced to enhance the stability of mechanical event localization under noise and motion interference. A structured electromechanical feature set is constructed to support fully automated processing. Results: Experimental results demonstrate that the proposed system maintains coherent event sequences and stable coupling parameter extraction across resting, movement, and emotional stress conditions. The incorporated LLM module integrates precomputed multimodal metrics under strict constraints, improving report readability and consistency without performing autonomous medical interpretation. Conclusions: This study demonstrates the methodological feasibility of an ECG–PCG coupling analysis framework for long-term cardiac state monitoring in low-resource environments. By integrating end-to-end automation, electromechanical coupling features, and constrained semantic reporting, the proposed system provides an engineering-oriented reference for continuous cardiac monitoring in community and home settings rather than a clinical diagnostic solution.

Chronic pain is a dynamic, brain-wide condition that eludes effective management by conventional, static treatment approaches. Transcutaneous Electrical Nerve Stimulation (TENS), traditionally perceived as a simple and generic modality, is on the verge of a significant transformation. Guided by advances in brain-state decoding and adaptive algorithms, TENS can evolve into a precision neuromodulation system tailored to individual needs. By integrating multimodal neuroimaging—including the spatial resolution of functional magnetic resonance imaging (fMRI), the temporal sensitivity of an Electroencephalogram (EEG), and the ecological validity of functional near-infrared spectroscopy (fNIRS)—with real-time machine learning, we envision a paradigm shift from fixed stimulation protocols to personalized, closed-loop modulation. This comprehensive review outlines a translational framework to reengineer TENS from an open-loop device into a responsive, intelligent therapeutic platform. We examine the underlying neurophysiological mechanisms, artificial intelligence (AI)-driven infrastructures, and ethical considerations essential for implementing this vision in clinical practice—not only for chronic pain management but also for broader neuroadaptive healthcare applications.

A wearable closed-loop transcutaneous electrical nerve stimulation (TENS) platform has been developed to address the limitations of conventional open-loop neuromodulation systems. Unlike existing systems such as CLoSES—which targets intracranial stimulation—and electromyography-triggered functional electrical stimulation (EMG-FES) platforms primarily used for motor rehabilitation, the proposed device uniquely integrates low-latency surface electromyography (sEMG)-driven control with six-channel current stimulation in a fully wearable, non-invasive format aimed at ambulatory pain modulation. The system combines real-time sEMG acquisition, adaptive signal processing, a programmable multi-channel stimulation engine, and a high-voltage, boost-regulated power supply within a compact, battery-powered architecture. Bench-top evaluations demonstrate rapid response to EMG events and stable biphasic output (±22 mA) across all channels with high electrical isolation. A human-subject protocol using the Cold Pressor Test (CPT), heart rate variability (HRV), and galvanic skin response (GSR) has been designed to evaluate analgesic efficacy. While institutional review board (IRB) approval is pending, the system establishes a robust foundation for future personalized, mobile neuromodulation therapies.

Abdominal massage for constipation has problems such as high labor costs, insufficient standardization, and large differences in efficacy. This study aims to develop a new type of abdominal massage robot to break through the bottlenecks of manual abdominal massage. This paper constructs a new six-degree-of-freedom multi-functional abdominal massage robot system, improves and designs a new series-parallel hybrid mechanical structure, and based on this, establishes an abdominal interaction force control strategy that uses a variable-stiffness physical model to represent the biomechanical properties of the abdomen. Verified by simulation and human experiments, this robot can accurately reproduce a variety of massage techniques, and demonstrate stable force control performance in prosthesis experiments and human abdominal tests, with the interaction force error controlled within 0.8 N, providing an innovative path for the intelligentization and standardization of clinical abdominal massage.

This research focuses on the crucial issue of ascertaining safe and suitable massage forces for abdominal massage robots. It does so by constructing variable stiffness soft tissue physical models and devising a machine learning-based prediction algorithm. Twelve healthy volunteers with diverse body types (underweight, standard, overweight) were enlisted. A self-developed experimental platform was utilized to gather high-precision mechanical data. Two physical models were formulated to depict the nonlinear force-displacement relationship of abdominal soft tissues. Model 1 was based on an exponential function, and Model 2 was based on a power function. Model 1 showed greater accuracy for standard body types (R² > 0.95 at 72.22% of data points) and underweight body types (R² > 0.95 at 100% of data points). In contrast, Model 2 was a better fit for overweight individuals (R² > 0.95 at 47.06% of data points). Furthermore, a transformer-based machine learning algorithm was developed. This algorithm predicts interaction forces using anthropometric and physiological characteristics. It achieved an accuracy of 92.60% within a pressing depth of 0–15 mm. In most situations, especially in full-range predictions (with an accuracy of 58.60%), the algorithm outperformed both physical models. This study presents a dual framework that combines biomechanical modeling and data-driven algorithms. It lays the theoretical and practical groundwork for safe and adaptable force control in abdominal massage robotics.

Virtual environments have been employed in the myoelectric prosthetics field as effective training and assessment tools to enhance intrinsic motivation, thereby encouraging sustained engagement in neuromuscular rehabilitation. However, motivating amputees to maintain consistent participation and perseverance in long-term training remains a critical challenge. To address this, we propose a scenario-guided adaptive incremental learning strategy that leverages contextual information in unknown environments to improve pseudo-label prediction accuracy. This strategy integrates two core components: Augmented Reality (AR) environment and Multimodal Progressive Domain Adversarial Neural Network (MPDANN). AR enables amputees to perform virtual prosthesis control and holographic object manipulation tasks in realistic, interactive scenarios, bridging the gap between laboratory training and daily-life usability. MPDANN Employs dual-domain classifiers through domain adversarial training, utilizing surface electromyography (sEMG) and inertial measurement unit (IMU) data to facilitate knowledge transfer across multi-source domains and achieve robust adaptation to unseen environments. A total of 16 able-bodied subjects and 2 amputee subjects completed a 5-day assessment protocol involving 10 holographic object manipulation tasks under 8 limb position conditions, using either a convolutional neural network (CNN) or MPDANN. Experimental results showed that able-bodied subjects using MPDANN achieved a 10 rate compared to the CNN baseline, reaching over 80 proficiency. While amputee subjects exhibited lower average completion rates than able-bodied subjects on the final day, the MPDANN strategy still demonstrated consistent performance improvements across both groups. This study substantiates the efficacy of integrating real-time visual feedback with a closed-loop domain adaptation algorithm, thereby enhancing sEMG recognition performance in untrained environments.

Co-adaptation interfaces, developed through user-machine collaboration, have the capacity to transform surface electromyography (sEMG) into control signals, thereby enabling external devices to facilitate or augment the sensory-motor capabilities of individuals with physical disabilities. However, the efficacy and reliability of myoelectric interfaces in untrained environments over extensive spatial range have not been thoroughly explored. We propose a user-machine closed-loop co-adaptation strategy, which consists of a multimodal progressive domain adversarial neural network (MPDANN), an augmented reality (AR) system and a scenario-based dynamic asymmetric training scheme. MPDANN employs both sEMG and Inertial Measurement Unit (IMU) data using dual-domain adversarial training, with the aim of facilitating knowledge transfer and enabling multi-source domain adaptation. The AR system allows users to perform 10 holographic object repositioning tasks in a stereoscopic mixed reality environment using a virtual prosthesis represented as an extension of the residual limb. The scenario-based dynamic asymmetric training scheme, which employs incremental learning in MPDANN and incremental training in the AR system, enables the continuous updating and optimization of the system parameters. A group of able-bodied participants and two amputees performed a five-day offline data collection in multiple limb position conditions and a five-day real-time holographic object manipulation task. The average completion rate for subjects utilizing MPDANN reached 83:37%±2:50% on the final day, marking a significant improvement compared to the other groups. These findings provide a novel approach to designing myoelectric interfaces with cross-scene recognition through user-machine closed-loop co-adaptation.