Lightweight Deep Multiscale Feature Learning for Improved Human Activity Recognition

Human Activity Recognition (HAR) is a significant field within human-computer interaction, often utilizing deep learning (DL) techniques for improved performance. However, DL's resource-intensive nature poses challenges, especially on memory-limited devices like smartphones and other edge devices. To address this issue, we propose a novel approach combining Wavelet Transform (WT) and DL. By decomposing smartphone signals using WT and feeding them into a Multiscale Con-volutional Neural Network (MSCNN), our method effectively extracts features at different scales for adaptive HAR without the need for manual feature extraction. This WT-MSCNN model offers advantages in processing non-stationary data, extracting HAR information effectively, and maintaining robustness to scale uncertainties in signals. Our experimental results indicate that the proposed methodology attains an accuracy of 98 %, a sensitivity of 99%, a specificity of 97%, and an Fl-score of 98.10% on NVIDIA Tesla T4 GPU (Graphics Processing Unit). Additionally, our model has demonstrated enhanced throughput, outperforming fine-tuned MobileNet-V3 and SqueezeNet Models by 83.59% and 25.72% respectively on the Raspberry Pi 4 hardware platform.