Human Activity Recognition in Smart Homes Using IMU Wristbands: A Comparative Study of Sampling Strategies and Machine Learning Models

This research explores Human Activity Recognition (HAR) for monitoring activities in smart homes using IMU wristbands, specific sampling strategies, and machine learning models to enhance accuracy. HAR systems are vital for identifying Activities of Daily Living (ADL), especially for elderly care. Data from 19 participants performing 14 activities in a smart apartment was collected using three-axis motion sensors. Preprocessing involved temporal windowing and statistical feature extraction, while dimensionality reduction was applied via PCA and SelectKBest. Four machine learning models (KNN, Random Forest, XGBoost, CatBoost) were trained with strategies like Leave-One-Subject-Out cross-validation. Longer temporal windows, such as 15 seconds, significantly improved performance. Activities like Brushing Teeth achieved an F1 score of 89%. While Chebyshev filtering had minimal impact, room-specific models and longer temporal windows demonstrated great potential for HAR in healthcare.