K-Equidistant Partitioning: Enhancing Sensor Data Analysis Using Innovative Data Augmentation Techniques
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In this study, K-equidistant partitioning (K-EP), a novel data augmentation method, is proposed to address the limitations of sensor data analysis and enhance the performance of behavior classification models. K-EP involves dividing rows of sensor data at equidistant intervals and extracting information from each segment, thereby increasing the size of the dataset by a factor of K. This method is based on the sensor minimum warranted frequency hypothesis, which posits that a sampling frequency of 20–40 Hz provides sufficient data for behavioral classification. The effectiveness of K-EP is validated through three experiments, which involve determining the optimal value of K for K-EP, comparing K-EP with other data augmentation methods, and assessing the added value of K-EP when combined with other methods. The results indicate that K-EP effectively overcomes the quantitative limitations of sensor data and enhances model robustness. It achieves higher F1-scores than existing methods, indicating that it is an effective data augmentation method for sensor-based behavior classification models.
Abstract
In this study, K-equidistant partitioning (K-EP), a novel data augmentation method, is proposed to address the limitations of sensor data analysis and enhance the performance of behavior classification models. K-EP involves dividing rows of sensor data at equidistant intervals and extracting information from each segment, thereby increasing the size of the dataset by a factor of K. This method is based on the sensor minimum warranted frequency hypothesis, which posits that a sampling frequency of 20–40 Hz provides sufficient data for behavioral classification. The effectiveness of K-EP is validated through three experiments, which involve determining the optimal value of K for K-EP, comparing K-EP with other data augmentation methods, and assessing the added value of K-EP when combined with other methods. The results indicate that K-EP effectively overcomes the quantitative limitations of sensor data and enhances model robustness. It achieves higher F1-scores than existing methods, indicating that it is an effective data augmentation method for sensor-based behavior classification models.