Background: The role of multifidus in the biomechanics of lumbar spine remained unclear. Purpose: This study aimed to investigate the role of multifidus in the modeling of lumbar spine and the influence of asymmetric multifidus atrophy on the biomechanics of lumbar spine. Methods: This study considered five different multifidus conditions in the trunk musculoskeletal models: group 1 (with entire multifidus), group 2 (without multifidus), group 3 (multifidus with half of maximum isometric force), group 4 (asymmetric multifidus atrophy on L5/S1 level), and group 5 (asymmetric multifidus atrophy on L4/L5 level). In order to test how different multifidus situations would affect the lumbar spine, four trunk flexional angles (0°, 30°, 60°, and 90°) were simulated. The calculation of muscle activation and muscle force was done using static optimization function in OpenSim. Then, joint reaction forces of L5/S1 and L4/L5 levels were calculated and compared among the groups. Results: The models without multifidus had the highest normalized compressive forces on the L4/L5 level in trunk flexion tasks. In extreme cases produced by group 2 models, the normalized compressive forces on L4/L5 level were 444% (30° flexion), 568% (60° flexion), and 576% (90° flexion) of upper body weight, which were 1.82 times, 1.63 times, and 1.13 times as large as the values computed by the corresponding models in group 1. In 90° flexion, the success rate of simulation in group 2 was 49.6%, followed by group 3 (84.4%), group 4 (89.6%), group 5 (92.8%), and group 1 (92.8%). Conclusions: The results demonstrate that incorporating multifidus in the musculoskeletal model is important for increasing the success rate of simulation and decreasing the incidence of overestimation of compressive load on the lumbar spine. Asymmetric multifidus atrophy has negligible effect on the lower lumbar spine in the trunk flexion posture. The results highlighted the fine-tuning ability of multifidus in equilibrating the loads on the lower back and the necessity of incorporating multifidus in trunk musculoskeletal modeling.

Continuous blood pressure (BP) monitoring has a significant meaning for the prevention and early diagnosis of cardiovascular disease. However, under different calibration methods, it is difficult to determine which model is better for estimating BP. This study was firstly designed to reveal a better BP estimation model by evaluating and optimizing different BP models under a justified and uniform criterion, i.e., the advanced point-to-point pairing method (PTP). Here, the physical trial in this study caused the BP increase largely. In addition, the PPG and ECG signals were collected while the cuff bps were measured for each subject. The validation was conducted on four popular vascular elasticity (VE) models (MK-EE, L-MK, MK-BH, and dMK-BH) and one representative elastic tube (ET) model, i.e., M-M. The results revealed that the VE models except for L-MK outperformed the ET model. The linear L-MK as a VE model had the largest estimated error, and the nonlinear M-M model had a weaker correlation between the estimated BP and the cuff BP than MK-EE, MK-BH, and dMK-BH models. Further, in contrast to L-MK, the dMK-BH model had the strongest correlation and the smallest difference between the estimated BP and the cuff BP including systolic blood pressure (SBP) and diastolic blood pressure (DBP) than others. In this study, the simple MK-EE model showed the best similarity to the dMK-BH model. There were no significant changes between MK-EE and dMK-BH models. These findings indicated that the nonlinear MK-EE model with low estimated error and simple mathematical expression was a good choice for application in wearable sensor devices for cuff-less BP monitoring compared to others.

Initial calibration is a great challenge for cuff-less blood pressure (BP) measurement. The traditional one point-to-point (oPTP) calibration procedure only uses one sample/point to obtain unknown parameters of a specific model in a calm state. In fact, parameters such as pulse transit time (PTT) and BP still have slight fluctuations at rest for each subject. The conventional oPTP method had a strong sensitivity in the selection of initial value. Yet, the initial sensitivity of calibration has not been reported and investigated in cuff-less BP motoring. In this study, a mean point-to-point (mPTP) paring calibration method through averaging and balancing calm or peaceful states was proposed for the first time. Thus, based on mPTP, a factor point-to-point (fPTP) paring calibration method through introducing the penalty factor was further proposed to improve and optimize the performance of BP estimation. Using the oPTP, mPTP, and fPTP methods, a total of more than 100,000 heartbeat samples from 21 healthy subjects were tested and validated in the PTT-based BP monitoring technologies. The results showed that the mPTP and fPTP methods significantly improved the performance of estimating BP compared to the conventional oPTP method. Moreover, the mPTP and fPTP methods could be widely popularized and applied, especially the fPTP method, on estimating cuff-less diastolic blood pressure (DBP). To this extent, the fPTP method weakens the initial calibration sensitivity of cuff-less BP estimation and fills in the ambiguity for individualized calibration procedure.

Surface electromyography (sEMG) provides a non-invasive measure of the neural drive transmitted from the central nervous system to muscles by capturing the spatiotemporal summation of motor unit action potentials at the skin surface, and is therefore widely used to study neuromuscular coordination during motor tasks. By reflecting neural drive transmitted from the central nervous system to peripheral muscles, sEMG provides valuable insights for investigating neuromuscular coordination during upper-limb motor tasks. Within the framework of modular motor control, muscle synergy analysis has been increasingly applied to characterize coordinated muscle activation patterns extracted from multi-channel sEMG recordings. In this study, sEMG signals were collected from twelve stroke patients and nine healthy subjects during robot-assisted upper-limb training, involving two movement trajectories (straight and rectangular) and multiple robot-assisted levels. Muscle synergies were extracted using non-negative matrix factorization (NMF). A synergy merging–splitting model, combined with a Functional Driving Ratio (FDR), was employed to characterize both the muscle synergy reorganization and the relative activation contributions of driving versus stabilizing muscle components in terms of motor control strategy. The results showed that healthy subjects maintained consistent muscle coordination patterns across different assistive levels, while making task-dependent adjustments to muscle activation to adapt to variations in movement trajectories. For stroke patients, higher functional status was correlated with more differentiated coordination patterns and relatively higher FDR values, suggesting greater reliance on task-relevant agonist muscles during movement execution. In contrast, lower-function patients exhibited less differentiated coordination patterns accompanied by reduced FDR values, indicating the increased involvement of stabilizing or antagonist muscles. This shift may reflect compensatory control strategies and the reduced efficiency of neuromuscular coordination during assisted upper-limb movements. These findings suggest that sEMG-based muscle synergy features and the FDR may provide quantitative, sensor-derived support for characterizing neuromuscular coordination during robot-assisted rehabilitation.