Quantitative Analysis of Mother Wavelet Function Selection for Wearable Sensors-Based Human Activity Recognition.

Area of Science:

• * Signal Processing
• * Machine Learning
• * Wearable Technology

Background:

• * Advancements in Internet of Things (IoT) wearable devices, particularly inertial sensors, necessitate precise human activity recognition (HAR).
• * Wavelet transform is suitable for HAR due to its time-frequency localization, but optimal mother wavelet selection is critical for performance.
• * Activity signals exhibit distinct periodic patterns, making mother wavelet shape matching to sensor data vital for recognition accuracy.

Purpose of the Study:

• * To propose and evaluate an optimal mother wavelet selection method for human activity recognition (HAR).
• * To investigate the impact of mother wavelet selection on HAR performance using wavelet packet transform and entropy-based criteria.
• * To provide guidelines for selecting optimal mother wavelets in HAR systems utilizing wearable inertial sensors.

Main Methods:

• * Implemented an optimal mother wavelet selection method combining wavelet packet transform with the energy-to-Shannon-entropy ratio.
• * Utilized decision tree (DT) and support vector machines (SVM) as classification algorithms.
• * Evaluated six mother wavelet families across eight public ADL datasets (MHEALTH, WISDM, HARTH, HARsense, DaLiAc, PAMAP2, REALDISP, HAR70+).

Main Results:

• * The proposed method demonstrated the significant impact of mother wavelet selection on HAR performance.
• * Optimal wavelet selection, guided by signal shape resemblance and energy-entropy ratio, improved recognition accuracy.
• * Analysis confirmed the effectiveness of the combined approach across diverse datasets.

Conclusions:

• * Optimal mother wavelet selection is a critical factor for enhancing HAR accuracy in wearable sensor systems.
• * The energy-to-Shannon-entropy ratio effectively guides the selection of wavelets that match activity signal characteristics.
• * This study offers a valuable framework for optimizing mother wavelet selection in HAR applications.