Wen-Fong Wang

The discomfort, invasiveness, and inconvenience associated with traditional glucose monitoring methods continue to hinder patient compliance and the effectiveness of diabetes management. This simulation-driven feasibility study presents the design of the Photoacoustic Glucose Monitoring Device (PAGMD), a non-invasive glucose sensing system that integrates photoacoustic spectroscopy, machine learning, and IoT connectivity to enable real-time glucose estimation. PAGMD employs pulsed near-infrared light to induce glucose-dependent acoustic signals, which are detected by ultrasonic transducers and analyzed by machine learning algorithms for concentration prediction. The device features a hybrid energy harvesting system, combining thermoelectric and piezoelectric elements, to support continuous, battery-assisted operation and long-term usability. The system was theoretically validated using a multi-stage simulation pipeline that included Monte Carlo photon modeling, finite element acoustic simulation, and synthetic glucose dataset generation across a range of physiological conditions. The machine learning models achieved a coefficient of determination of 0.98 and a mean absolute relative difference of 6.97%, with 98.2% of predictions falling within clinically acceptable error zones. While current findings are based on simulated environments, a future validation roadmap encompassing in vitro, ex vivo, and in vivo studies is proposed to support clinical translation. This work should be regarded as a simulation-driven feasibility study; the reported benchmarks represent design targets for future prototyping rather than experimentally validated hardware results. By prioritizing non-invasiveness, ease of use, and adaptive intelligence, PAGMD represents a promising step toward inclusive, personalized metabolic health monitoring, especially for individuals with disabilities or limited dexterity.

Photoplethysmography (PPG) is well suited for wearable health applications, but has a lower frequency spectrum than electrocardiography (ECG) and is more affected by motion artifacts. In this study, ten signal filters from three categories were investigated in combination with different sampling rates to evaluate their effects on PPG signal quality. A correlation and accuracy analysis was performed comparing the interbeat intervals detected in PPG and ECG using Pearson correlation and absolute error. The results showed that specific filters with sampling rates as low as 40 Hz perform well in detecting autonomic neuropathy. The results highlight the potential of PPG with optimized filters and sampling rates for clinical screening of the autonomic nervous system (ANS) in wearable health monitoring.

Frailty in older adults is caused by functional declines that result in unstable gait. This study analyzed gait in 24 frail and 22 non-frail older adults using acceleration and angular velocity signals from a wireless tri-axial inertial measurement unit (IMU). After noise was removed through Savitzky-Golay and Butterworth filters, gait features correlated with frailty were proposed and evaluated through normality tests and statistical analysis. To evaluate the frailty of older adults based on significant gait features derived from statistical analysis, the primary accuracy achieved is roughly around 84–89 % in k-nearest neighbor, support vector machine, and random forest models. To provide clinicians with a good tool for monitoring frailty and support preventive healthcare and aging-in-place strategies, we propose a gait-based detection system with an optimal feature extraction scheme that can exhaustively enumerate and evaluate potential parameters for optimal performance. This system significantly improved classification metrics (nearly all >95 %) with lower sensitivity and specificity and achieved 96 % accuracy with a portable, low-cost system that uses only one minute of walking data. These findings demonstrate that IMU-based gait analysis improves objectivity and accuracy in frailty classification. The optimal feature extraction scheme further refines performance, offering a scalable and time-efficient solution for community-based frailty detection. This approach highlights the potential of wearable sensors in improving geriatric health assessments.

Background: Sarcopenia, gait disturbance, and intradialytic hypotension are among the various factors that contribute to fall risk. This study aimed to investigate the relationship between risk of sarcopenia, hemodialysis (HD) session, and long-term fall risk in older end-stage kidney disease (ESKD) patients by analyzing their spatiotemporal gait characteristics. Methods: We recruited 22 non-demented patients aged ≥ 65 years who were undergoing maintenance HD. Participants were divided into two groups based on their SARC-F score (< 4 and ≥ 4) to identify those with higher and lower risk of sarcopenia. Demographics, comorbidities, and renal parameters were compared between groups. Inertial measurement unit-based technology equipped with triaxial accelerometry and gyroscope was used to evaluate gait characteristics. The gait task was assessed both before and after dialysis using the Timed-Up and Go (TUG) test and a 10-meter walking test at a regular pace. Essential gait parameters were thoroughly analyzed, including gait speed, stride time, stride length, double-support phase, stability, and symmetry. We investigated the interaction between the dialysis procedure and gait components. Outcome of interest was any occurrence of injurious fall during follow-up period. Logistic regression models were employed to examine the relationship between baseline gait markers and long-term fall risk. Results: The SARC-F ≥ 4 group showed various gait abnormalities, including longer TUG time, slower gait speed, longer stride time, shorter stride length, and longer double support time compared to counterpart (SARC-F < 4). After HD sessions, the SARC-F ≥ 4 group showed a 2.0-second decrease in TUG task time, an 8.0 cm/s increase in gait speed, an 11.6% lower stride time, and a 2.4% increase in gait symmetry with significant group-time interactions. Shorter stride length and longer double support time were associated with injurious falls during the two-year follow-up. Conclusion: Our study demonstrated the utility of triaxial accelerometers in extracting gait characteristics in older HD patients. High-risk sarcopenia (SARC-F ≥ 4) was associated with various gait abnormalities, some of which partially improved after HD sessions. These gait abnormalities were predictive of future falls, highlighting their prognostic significance.

This study aimed to use the k-nearest neighbor (kNN) algorithm, which combines gait stability and symmetry derived from a normalized cross-correlation (NCC) analysis of acceleration signals from the bilateral ankles of older adults, to assess fall risk. Fifteen non-fallers and 12 recurrent fallers without clinically significant musculoskeletal and neurological diseases participated in the study. Sex, body mass index, previous falls, and the results of the 10 m walking test (10 MWT) were recorded. The acceleration of the five gait cycles from the midsection of each 10 MWT was used to calculate the unilateral NCC coefficients for gait stability and bilateral NCC coefficients for gait symmetry, and then kNN was applied for classifying non-fallers and recurrent fallers. The duration of the 10 MWT was longer among recurrent fallers than it was among non-fallers (p < 0.05). Since the gait signals were acquired from tri-axial accelerometry, the kNN F1 scores with the x-axis components were 92% for non-fallers and 89% for recurrent fallers, and the root sum of squares (RSS) of the signals was 95% for non-fallers and 94% for recurrent fallers. The kNN classification on gait stability and symmetry revealed good accuracy in terms of distinguishing non-fallers and recurrent fallers. Specifically, it was concluded that the RSS-based NCC coefficients can serve as effective gait features to assess the risk of falls.