Enhancing Sensor-Based Human Activity Recognition With Multiresolution Wavelet-Attention

Sensor-based Human Activity Recognition (SHAR) is a research field that leverages wearable sensors for the continuous monitoring of human behavior, for example in sports, household, and healthcare environments. This paper introduces a novel SHAR approach that extends data-driven methods with sophisticated signal analysis. We propose a pre-trained Multi-Resolution Wavelet-Attention (MRWA) neural network block that learns multi-level Wavelet coefficients from SHAR signals and refines them using a cross-attention mechanism. The evaluation on eight widely-used benchmark datasets demonstrates that the combination of MRWA with existing SHAR classifiers significantly improves the detection of key signal features, boosting macro F1-scores by up to 0.286 points for DeepConvLSTM, 0.079 points for GRU, and 0.033 points for TinyHAR, all while increasing their inference times by less than 70 milliseconds. This keeps the MRWA-enriched classifiers efficient for deployment on current smartwatches. Additionally, MRWA improves the robustness and reliability of SHAR classifiers by keeping the standard deviations of the F1-scores low across multiple scenarios, thus providing a more accurate and efficient solution for seamless activity recognition.