Key result
SV4PE integrates IMU and ECG sensors to objectively assess children's athletic performance.
Why the study?
Objective, quantifiable metrics to evaluate children's health and athletic performance during Physical Education classes are currently lacking.
The SV4PE wearable system demonstrates preliminary capability to objectively monitor and assess children's physiological and biomechanical performance during physical activities.
May support objective monitoring in pediatric physical education; leaves open validation of accuracy and utility in larger prospective studies.
There is currently a lack of objective, quantifiable metrics to evaluate children's health and athletic performance during Physical Education classes. To address this gap, the TexP@ct Consortium is developing a Smart Vest for Physical Education (SV4PE)―a textile engineered wearable solution that integrates a single triaxial Inertial Measurement Unit (IMU) and electrocardiogram (ECG) sensors, embedded at the T8 spinal level. Designed for comfortable and unobtrusive use, the SV4PE enables recording and analysis of biomechanical and physiological data during physical activity. This paper presents the preliminary system validation and algorithm development for the SV4PE system, detailing the sensor fusion and signal processing methods used to extract metrics from live and recorded data, along with results from experimental and prototype datasets. The algorithms designed measure an athlete's heart rate, movement intensity, and effort, with additional post-exercise metrics to characterize fundamental movements such as walking, running, and jumping. Sensor fusion packages were implemented, combining acceleration and angular velocity, to correct sensor drifts and remove gravity components. Following filtering and resampling, walking and running metrics, such as cadence, distance and velocity, are extracted through gait event identification, using wavelet transforms. Jumping characteristics are derived from vertical acceleration using projectile motion equations to estimate jump height, take-off force, and power output. Lastly, heart rate is calculated from QRS peak detection in the ECG signal, and associated with subject metadata to evaluate exercise intensity and effort levels. Additional algorithms are under-development to assess fitness tests (e.g., mile run, shuttle run, push-ups, etc.), for team sport motion classification using machine learning, and for player localization within a playfield for detailed performance analysis. Ultimately, this work seeks to provide teachers and trainers with practical tools to objectively monitor and assess children's performance during sports and physical activities.
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Argueta et al. (2025) studied Physical Education / Athletic performance. Smart Vest for Physical Education (SV4PE) was evaluated on System validation and algorithm development for extracting biomechanical and physiological metrics. The Smart Vest for Physical Education (SV4PE) integrates IMU and ECG sensors to extract objective biomechanical and physiological metrics for assessing children's athletic performance.
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