Stress affects health and well-being, with heart rate (HR) and heart rate variability (HRV) recognized as the key indicators of the autonomic nervous system (ANS), which are parameters controlling stress responses. While most studies focused on only one to two parameters, this study explored the interplay of three, namely, physiological, psychological, and physical stress responses from wearable technology data. The employment of the Gaussian Mixture Model (GMM), Uniform Manifold Approximation and Projection (UMAP) Manifold Learning, and statistical analysis allowed the research to uncover hidden patterns of stress responses, assessing stress severity and binary classifications. Significant overlaps in moderate stress levels were observed between the mild and severe levels, confirming challenges in distinguishing stress levels. This led to the adoption of binary classifications, enhancing robustness and simplifying the outputs. Through statistical validation, it confirmed strong correlations between UMAP components and stress severity. Physiological markers, including HR, electrodermal activity (EDA), and accelerometer data (ACC), consistently exhibited high correlations with severe stress levels. While skin temperature (TEMP) and interbeat intervals (IBI) contributed to moderate stress differentiation. Finally, mild stress has a notable connection to blood volume pulse (BVP), IBI, and HR.

Lung diseases are one of the leading causes of global morbidity and mortality, accounting for more than four million deaths annually according to the World Health Organization. Although machine learning has shown promise in automating lung sound classification, most existing models are either focused on binary lung sound classification or have a narrow subset of abnormal sounds, hindering their diagnostic utility. This study addresses these limitations by developing a high-SNR, multiclass, and accurate classification model capable of identifying normal lung sounds and five abnormal types: wheezes, crackles, stridor, rhonchi, and pleural rubs. Various feature extraction techniques were compared, including the standard Mel-Frequency Cepstral Coefficients (MFCC) model, an enhanced-MFCC (eMFCC) model, and a hybrid Discrete Wavelet Transform-Short-Time Fourier Transform (DWT-STFT). The input-output Signal-to-Noise Ratio (SNR) analysis confirmed a significant improvement in signal quality, with median SNR increasing from 16 dB (MFCC) to 35 dB (eMFCC), validating the effectiveness of the enhancement techniques. Classification was performed using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), with the highest accuracy of 97.69 % and 98.34 %, respectively, achieved using eMFCC mean features. The results demonstrate the robustness of the proposed high-SNR feature-based model for accurate multiclass lung sound classification.

Patellofemoral Pain Syndrome (PFPS) is a condition that causes pain at the front of the knee, particularly affecting active individuals such as athletes and military personnel. Accurate diagnosis remains challenging, as Magnetic resonance imaging (MRI) methods are often costly. Various applications, including preprocessing and segmentation techniques, have assisted clinicians by improving the quality of patellar tendon imaging. However, clinical observations and measurement of PT-TG distance were affected by factors like practitioner probe angles and patient skin thickness. Therefore, this study aimed to aid clinicians by analyzing key features from ultrasound (US) imaging, focusing on textural and morphological characteristics alongside biological markers. Datasets were collected by the Research Center for Health Sciences at the University of Santo Tomas and included twenty-seven participants, fourteen with PFPS and thirteen without PFPS. Fifty-one features were extracted and analyzed through Feature selection techniques, including Statistical Analysis with Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), and Mutual Information in selecting the most optimal features. These features were validated using sensitivity, specificity, recall, and accuracy. Twenty-three features were selected using statistical analysis with PCA, composed of fifteen textural features, four morphological features, and four biological features were selected. The final model achieved an F1 score of 89% for classifying non-PFPS and$\mathbf{8 1 \%}$for classifying people with PFPS, with overall accuracy of 86%. By analyzing these selected features, the study aims to enhance the evaluation of ultrasound images and biological markers for PFPS detection, contributing to better patient care.

Hamstring muscles are commonly injured in sports involving strenuous activities. Evaluating the muscle condition is done through physical examinations like ultrasonography and MRI, which provide accurate assessments but are costly. An alternative method in obtaining information from muscle activity is through Surface Electromyography (sEMG) signals, which is recorded through electrical signals. Particularly, the nonlinear features are assessed to monitor muscle activity in the hamstring since these can determine intrinsic changes in muscle behavior. These nonlinear features bring about high dimensionality to the signal, which requires manifold learning to reduce its dimensionality and extract its underlying nonlinear features and relationships. In this paper, the researchers propose a solution that provides an automated classification of HSI based on nonlinear features of sEMG signals using machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LoR), along with manifold learning techniques such as t-distributed Stochastic Neighborhood Embedding (t-SNE) and Locally Linear Embedding (LLE). The study results showed that SVM was considered the best overall predictive model for classification, with an accuracy of 88.51% when applied on t-SNE and 75.40% when applied on LLE.

Inertial measurement unit (IMU) sensors are widely used in sports science to acquire motion-related data, such as joint angle measurements, and provide valuable insights into the performance of the athletes. These sensors can be used for specific purposes that needs regular calibration to compensate for drift since assessment of muscle stress rely on these devices. However, sensor-based systems may not always provide accurate joint angle measurements. This paper aims to mitigate the drift of the IMU data by pre-processing it with various sensor fusion algorithms: Complementary filter, Kalman filter, and Madgwick filter. The output of the calibrated IMU sensor device was compared with the data collected from the VICON motion capture system to validate its accuracy. Finally, the calibrated IMU sensor output is used to measure the joint angles. As a result, the complementary filter resulted in a better accuracy and reliability performance than the Kalman and Magdwick filter. The Kalman filter obtained a higher reliability and precision due to its lower variability and standard deviation of 37.68. Moreover, it also indicated that the complementary filter often yielded low error values, suggesting better performance as seen in its Root- Mean-Square Error (RMSE) of 9.75. An Analysis of Variance (ANOVA) test was done, indicating that the RMSE accepts the null hypothesis, H0, that there are no significant differences among the means of the metrics.

Surface electromyography (sEMG) is crucial in sports science, offering insights into muscle activation patterns. However, standard devices like the Delsys Trigno Wireless System are relatively costly. This study presents a prototype that simultaneously obtains sEMG and IMU signals to measure muscle activity. The presence of noise necessitates signal processing techniques to be applied to the raw signal, including feed-forward comb, adaptive, and wavelet filters. Evaluation against a reference signal revealed that the wavelet filter performed best, exhibiting the lowest MSE and highest SNR scores. Specifically, it achieved MSE scores of 2.62E-10 in the time domain, 6.25E-22 in PSD, and SNR scores 13.96 for squats. These findings underscore the effectiveness of the wavelet filter in reducing sEMG signal noise.

Mental health disorders, or neuropsychiatric disorders, encompass disturbances in cognition, emotion, behavior, and physical well-being, with depression being one of the most prevalent. The subcortical limbic brain regions, particularly the amygdala, hippocampus, and dorsomedial thalamus, are profoundly impacted in depressed individuals. Music therapy has emerged as a promising intervention for various health issues, including depression, demonstrating substantial improvements in mental health, emotional well-being, and overall quality of life. Despite its global recognition, music therapy remains underutilized in the Philippines. This study aims to quantitatively assess the neu-rophysiological effects of music on patients diagnosed with depression using EEG-based neural manifold learning. This paper implements neural manifold learning and analysis on two datasets to quantitatively assess the effects of music on brain states and to characterize the effects of depression on neural manifolds. Our analysis revealed that distinct features of music provide a compelling effect on the neural manifold structures, suggesting a relationship between musical properties and brain dynamics. This research seeks to advance the understanding of music therapy's potential as a viable treatment for depression, particularly within the Philippine context.

Patellofemoral Pain Syndrome (PFPS), characterized by anterior knee pain around the patella, requires accurate diagnosis for effective management. Traditional methods rely on clinician expertise, which can be subjective and time-consuming. This study introduces an automated system to measure lateral patellar tendon displacement, specifically the Patellar Tendon-Trochlear Groove (PT-TG) distance, using preprocessed ultrasound images to diagnose PFPS. While Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) offer high-resolution imaging, they are costly and carry risks such as radiation. Ultrasound imaging, on the other hand, is non-invasive, cost-effective, and convenient, allowing for repeated examinations. However, ultrasound images often suffer from low contrast, blurred edges, speckle noise, and shadows, complicating accurate segmentation and analysis. Therefore, image preprocessing is crucial to enhance ultrasound image quality to further extract the medical values embedded within the images. This study applies Contrast Limited Adaptive Histogram Equalization (CLAHE) and Gamma Correction for contrast enhancement, along with Rank Order Filter for speckle noise reduction. Data were collected from the Research Center for the Health Sciences at the University of Santo Tomas, involving 26 participants, 12 with PFPS and 14 without. Results indicate that these preprocessing algorithms significantly enhance image quality, leading to better segmentation of the patellar tendon and trochlear sulcus, and more accurate PT-TG distance measurement. The error rate in measuring lateral patellar tendon displacement decreased from 54.13% to 25.91% post-preprocessing. The proposed algorithm shows potential in aiding PFPS diagnosis, reducing the diagnostic burden on clinicians, and potentially improving patient outcomes.

The hamstring muscle is frequently susceptible to injury among athletes across various sports. This paper investigates the use of multidimensional scaling (MDS) to project data points derived from IMU and sEMG data into a lower dimensional space to characterize hamstring muscle activation during leg squats. The research aims to assemble a wearable sensor system for hamstring data acquisition and to characterize the extracted features, including the knee angle, thigh Euler angles, and sEMG signals, analyzing trends and behaviors in the manifold representation. Then, recurrence quantification analysis (RQA) was used to generate recurrence plots, revealing relevant insights into the underlying dynamics, predictability, and complexity of hamstring muscle activity. A t-test on the RQA parameters between rest and active states showed significant differences, indicating distinct behaviors between these states.