Ensemble Learning with Heterogeneous Models for Human Activity Recognition in Missing Sensor Scenarios

Contemporary smartphones and wearable devices are equipped with various sensors, and considerable research has focused on human activity recognition (HAR) tasks and attempts to infer user context from sensor data. However, a major challenge in sensor-based HAR is the presence of missing sensor data. Depending on the smartphone model, certain sensors may be unavailable, and sensor degradation or failure can also result in partial data loss. In such cases, conventional activity recognition methods exhibit a considerable reduction in accuracy compared with scenarios where all sensors are available.