Sensor-based human activity recognition using wrist and ankle wearables

Abstract

This article proposes using dual-body sensor placement on the right ankle and wrist with raw multi-sensor fusion (accelerometer, magnetometer, and gyroscope at 66.6 Hz) to capture complementary upper- and lower-body kinematics across nine complex daily activities and real-time integration with smart homes. Two independent datasets were obtained from participants performing different activities each at intervals of 5–10 min and with real-time labeling. Classification was carried out using six different machine learning architectures (SVM, Logistic Regression, ANN, C&RT, Random Forest, and C5.0). Random Forest and Multilayer Perceptron (ANN) models demonstrate superior cross-dataset generalization, achieving 93.48% accuracy when evaluated on fully independent sessions. Predictor importance analysis reveals distinct kinematic dependencies, where leg magnetometers dominate elevation-based activities (stair climbing) and wrist accelerometers drive fine-motor actions (typing, writing). Integrating a 120-sample majority-voting filter effectively eliminates transient classification noise without compromising real-time responsiveness (< 2s latency). Finally, the framework is validated via C#, PMML extraction, and Falcon.NET integration for real-time KNX smart home control. The study findings highlight the feasibility of integrating wearable HAR systems into smart homes for real-time monitoring and adaptive assistance.