Intelligent sensing technologies are transforming sports training by enabling precise motion analysis, critical for skill development and performance optimization. This study introduces a badminton racket handle embedded with a lightweight, multinode MEMS-based sensing system designed for real-time motion recognition. To capture distributed grip forces, swing trajectories, and impact mechanics at the player-equipment interface, the system employs an ergonomic design ensuring natural gameplay. A hybrid feature extraction approach, integrating time- and frequency-domain features with a 1-D-convolutional neural network (CNN), achieves a classification accuracy of 97.89% across ten badminton actions. To enhance interpretability and provide actionable insights, explainable AI using SMDL-attribution identifies key motion features, revealing biomechanical inefficiencies in grip strength, swing consistency, and wrist motion. Seamlessly integrated with virtual reality (VR) platforms, the system delivers immersive, real-time feedback, transforming training into an interactive and data-driven experience. By combining advanced sensing, machine learning, and explainable AI, this system establishes a new benchmark for intelligent sports monitoring, with broad applications in sports training, rehabilitation, and human-computer interaction.

Ballistocardiography (BCG) enables unobtrusive physiological monitoring, but its signal quality is sensitive to body–sensor coupling and posture. This study proposes a dual-channel quality assessment framework for seated BCG and measurement-configuration evaluation. A chair-integrated Mach–Zehnder interferometer fiber-optic sensing system recorded back and hip BCG signals from 11 participants at five backrest angles (100°, 120°, 140°, 160°, and 180°). Thirty candidate features were extracted from 15 s windows, and channel-specific three-class quality models were developed using selected interpretable features and a bagged-trees classifier under repeated subject-wise evaluation. Classification probabilities were mapped to continuous quality scores and integrated with JJ-interval consistency and cross-channel delay stability to construct a dual-channel quality index. The models distinguished low-, medium-, and high-quality windows, while the joint index characterized differences in dual-channel analyzability across measurement configurations. Angle-related differences were not statistically significant when all windows were included but became significant after excluding low-quality windows. Within the tested cohort and experimental configuration, 140${}^{\circ }$ showed the highest descriptive overall quality, followed by 160${}^{\circ }$, whereas 100${}^{\circ }$ performed worst. The angle ranking was largely stable across alternative weighting schemes. However, cross-channel consistency does not constitute independent physiological validation, and improvements in downstream parameter-estimation accuracy remain unverified. The proposed framework supports quality-guided evaluation of seated BCG acquisition configurations and warrants further validation against independent physiological references.

Mental fatigue is commonly studied using prolonged cognitive tasks, but physiological changes observed in single-group paradigms may also reflect time-on-task, circadian variation, nap deprivation, posture, and cognitive load. This exploratory study characterized continuous heart-rate variability (HRV) dynamics during a 60-min 2-back task performed in the habitual afternoon nap period. Twenty-six healthy graduate students completed pre- and post-task assessments using the mental-fatigue subscale of the Fatigue Self-Assessment Scale (FSAS) and a simple reaction-time test; 22 participants were included in the HRV analysis after four recordings were excluded because insufficient compliance with the required seated protocol resulted in inadequate valid data. Ballistocardiogram signals were acquired using a 1000-Hz fiber-optic sensing cushion. Nineteen HRV features were estimated in 5-min windows with a 1-min step, and temporal dynamics were described using baseline-relative change (Delta) and log change rate (LCR). The FSAS mental-fatigue score increased from $18.99\pm 17.09$ to $46.88\pm 24.89$ (mean change 27.88, 95% CI 19.34–36.43; paired $t\left(25\right)=6.72$, $p<0.001$; Cohen’s $d_z=1.32$), and reaction time increased from $371.63\pm 37.74$ to $407.62\pm 66.70$ ms (mean change 35.98 ms, 95% CI 21.17–50.80; $t\left(25\right)=5.00$, $p<0.001$; $d_z=0.98$). A BCG-derived respiratory surrogate showed a phase effect for relative respiratory amplitude but not respiratory rate. Across the 19 HRV features included in the primary analysis, several nominal phase effects were observed, but none remained significant after Benjamini–Hochberg false-discovery-rate correction. Exploratory ranking and dynamic time-warping clustering indicated heterogeneous feature trajectories, although the fixed five-cluster solution was not the best-separated solution and participant-level reproducibility was limited. These findings support the feasibility of unobtrusive BCG for describing continuous physiological variation during prolonged cognitive engagement, but they do not establish fatigue-specific HRV phases, diagnostic markers, or individual-level monitoring performance.

Radiation-induced chronic wounds remain a major clinical challenge owing to persistent non-healing and progressive tissue degeneration. Rather than simply representing delayed wound repair, they arise from a self-perpetuating pathological cascade involving DNA damage, oxidative stress, chronic inflammation, microvascular dysfunction, cellular senescence, stem cell impairment, and fibrosis. Current clinical management remains largely supportive and symptomatic, with limited efficacy and no standardized therapeutic regimens targeting the core pathological mechanisms. Emerging strategies—antioxidant, antifibrotic, senolytic, stem cell/exosome-based, and functional biomaterial approaches—show promise. This review critically summarizes recent advances in pathogenic mechanisms and mechanism-based therapeutic strategies, highlighting current translational challenges and future directions for microenvironment-oriented regenerative therapies.