Robust Human Activity Prediction Through Multi-View Sensor Data Integration

Human Activity Prediction (HAP) systems play a crucial role in assisting caregivers by enabling timely assistance and preventive interventions for the elderly. In this work, we present a multi-view approach to HAP that leverages data from multiple sensor sources to enhance system robustness against noise and missing sensor data. We evaluate our approach for data integration using Random Forests and LSTM models on two real-world datasets, demonstrating that integrating heterogeneous sensor streams allows the system to exploit their complementary nature. Our experimental results show that the multi-view approach significantly improves prediction accuracy, with LSTM networks benefiting more substantially from multiple views compared to Random Forest models. We also investigate the impact of missing sensor data, a common challenge in real-world deployments, and demonstrate that Random Forest models exhibit greater resilience to sensor failures. Additionally, we address the challenge of class imbalance through SMOTE, which proves to be particularly effective for LSTM models. This work provides empirical evidence for the benefits of multi-view learning in HAP systems, particularly in environments where sensor reliability cannot be guaranteed.