Existing data acquisition systems for lower-limb assessment typically rely on single-sensor modalities and devicespecific protocols, which limit accurate measurement of both knee angles and peak muscle activations during exercises like standing leg curls. To address these limitations, we developed a wireless device integrating IMU and sEMG sensors, synchronizing data via timestamps and dynamic time warping. The Kalman filter is used to obtain the IMU signals, while an adaptive filter is used to denoise the sEMG signals. From the combined IMU and sEMG recordings, clear trends emerge in peak leg curl power as a function of knee flexion angle; to visualize these patterns, we applied K-means clustering (k$=3)$to knee flexion angle$\left(40^{\circ}-115^{\circ}\right)$versus peak leg curl power ($0.2-1.0 ~\mathrm{W}$) across all repetitions, which revealed three regimes, low ($\approx 60^{\circ}-80^{\circ}, 0.5-0.8 ~\mathrm{W}$), medium ($\approx 80^{\circ}-95^{\circ}$,$0.7-0.9 ~\mathrm{W}$), and high ($\approx 95^{\circ}-115^{\circ}, 0.7-1.0 ~\mathrm{W}$), highlighting typical performance zones. Analysis of the resulting scatter plot showed peak muscle activation during concentric phases at flexion angles of$\mathbf{9 7}^{\boldsymbol{\circ}} \boldsymbol{-} \mathbf{1 1 3}^{\boldsymbol{\circ}}$. Prototype knee-angle measurements achieved RMSE 7.37°, MAE 4.76${ }^{\circ}$, ICC 0.98, and Spearman's$\rho=0.9765$against ground truth, confirming the system's reliability in data acquisition. Overall, the integrated system provided reliable measurements of muscle activation and knee angles with acceptable error margins and consistency.

Human presence detection has various applications, with most existing methods relying on fixed-frequency RF signals like Wi-Fi, limiting transmitter-receiver distance. This study investigates the feasibility of using 4G Long Term Evolution (LTE) bands such as bands 28, 3, 1, and 41 for human presence detection in indoor environments. LTE signal data were collected during video call sessions using a Software-Defined Radio (SDR). Support Vector Machine (SVM) models were trained and evaluated on 0 vs. 1 person, 0 vs. 3 person, and 0 vs. 1 vs. 3 or multiple classifications per band. Results show that band 41, which has the highest frequency range, provides better accuracy in detecting human presence for 0 vs. 3 and multi-person classification, while Band 28, which has the lowest frequency range, performs better for detecting no-person scenarios. This highlights the impact of LTE frequency variation on human presence and crowd density detection.

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.

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.