Wearable Sensor-Based Sport Activity Recognition Using Machine Learning Classification Techniques
DOI:
https://doi.org/10.12928/biste.v8i5.16855Keywords:
Sport Activity Recognition, Wearable Sensors, Machine Learning Classification, Support Vector Machine (SVM), Sensor Data FusionAbstract
Sport activity recognition using wearable sensors is important for monitoring human movement and supporting sport-performance analysis. This study presents a machine-learning framework for recognizing basketball, soccer, and volleyball activities using wearable sensor data collected from 23 subjects. Sensors were placed on the right arm and right leg to record accelerometer, gyroscope, linear acceleration, and temperature signals representing upper- and lower-body movements. Several supervised machine-learning classifiers were evaluated using cross-validation and standard classification metrics. The results showed that Support Vector Machine (SVM) achieved the best overall performance, with an accuracy of 84.796%, macro-precision of 0.84827, macro-recall of 0.84796, and macro-F1 score of 0.84795. K-Nearest Neighbors (KNN) achieved the second-highest accuracy of 81.344%, followed by the Ensemble classifier with 78.180%. The findings indicate that SVM is more effective in separating the partially overlapping and nonlinear motion patterns associated with the selected sport activities. Beyond classifier comparison, the study demonstrates the effectiveness of dual-location multimodal wearable sensing and comprehensive model evaluation for distinguishing sport activities with partially overlapping movement patterns.
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