Jinting Ma

Hemiplegic shoulder pain (HSP) is a common complication following stroke, significantly affecting upper limb recovery and quality of life. However, the underlying pathophysiological mechanisms of HSP remain poorly understood, which poses a major obstacle to the development of effective therapeutic strategies. This study aims to investigate the underlying mechanism of HSP by evaluating neuro-musculo-vascular activities using high-density surface electromyography (HD-sEMG), musculoskeletal ultrasound, and laser speckle contrast imaging (LSCI). A total of 12 HSP patients and 12 hemiplegic controls without shoulder pain (HNSP) participated in this study. Their neuro-musculo-vascular data in the affected shoulder were collected using a 64-channel HD-sEMG electrode array, a musculoskeletal ultrasonic probe, and a LSCI sensor. Muscle activity was quantified by the root mean square (RMS) of HD-sEMG signals, while neural firing activity was characterized by the discharge rate and coefficient of variation (CoV), decomposed from the HD-sEMG. Meanwhile, structural characteristics were measured by the shoulder subluxation distance (SSD) from ultrasonic image, and blood perfusion was evaluated by perfusion units (PU) from LSCI. Results showed significantly lower RMS and CoV in HSP group versus HNSP group (p0.05), while SSD negatively correlated with both RMS and CoV (p<0.05). These findings suggest that HSP is more closely related to neuromuscular control abnormalities and shoulder joint instability than to microcirculatory dysfunction, emphasizing the need for targeted neuromuscular rehabilitation in treating HSP.

Continuous blood pressure (BP) monitoring is essential for the early detection and prevention of cardiovascular diseases like hypertension. Recently, interest in continuous BP estimation systems and algorithms has grown. Various physiological signals reflect BP variations from different perspectives, and combining multiple signals can enhance the accuracy of BP measurements. However, research integrating electrocardiogram (ECG), photoplethysmography (PPG), and impedance cardiography (ICG) signals for BP monitoring remains limited, with related technologies still in early development. A major challenge is the increased system complexity associated with acquiring multiple signals simultaneously, along with the difficulty of efficiently extracting and integrating key features for accurate BP estimation. To address this, we developed a BP monitoring system that can synchronously acquire and process ECG, PPG, and ICG signals. Optimizing the circuit design allowed ECG and ICG modules to share electrodes, reducing components and improving compactness. Using this system, we collected 400 min of signals from 40 healthy subjects, yielding 4390 records. Experiments were conducted to evaluate the system’s performance in BP estimation. The results demonstrated that combining pulse wave analysis features with the XGBoost model yielded the most accurate BP predictions. Specifically, the mean absolute error for systolic blood pressure was 3.76 ± 3.98 mmHg, and for diastolic blood pressure, it was 2.71 ± 2.57 mmHg, both of which achieved grade A performance under the BHS standard. These results are comparable to or better than existing studies based on multi-signal methods. These findings suggest that the proposed system offers an efficient and practical solution for BP monitoring.