Leveraging Residual Deep Neural Networks and Multi-Device Sensors for Heterogeneous Activity Recognition

This study introduces a novel approach to identifying human activities using wearable sensors, particularly smart-phones and smartwatches. By leveraging deep learning neural networks and data from the HHAR dataset, which includes accelerometer and gyroscope data from individuals engaged in various activities, our method, centered around the HAR-Res NeXt model, accurately detects six activities. Utilizing residual connections and multi-kernel blocks, our approach effectively captures temporal and spatial relationships in sensor data. Experimental results demonstrate superior performance to standard machine learning algorithms and other deep learning approaches for human activity recognition. HAR-ResNeXt achieves high accuracy rates, particularly in classifying smartphone sensor data, underscoring its adaptability across diverse scenarios. Comparative analysis reveals the effectiveness of smartphone sensors and emphasizes the importance of multi-modal sensor fusion for accurate activity detection.