Expanding the Horizons of 1D Tasks: Leveraging 2D Convolutional Neural Networks Pre-Trained by ImageNet

Sensor-based human activity recognition (HAR) plays a pivotal role in the Internet of Things (IoT). Due to the low cost, low power consumption, and broad applicability of sensor devices, the integration of HAR with IoT has become increasingly imminent. Deep learning (DL) based HAR methods, with their capability for automatic feature extraction, are applied in our daily lives. However, obtaining a substantial amount of labeled data for HAR tasks remains a challenge. Although transfer learning (TL) offers a viable solution in the HAR domain, we lack large-source domain datasets akin to ImageNet. In response to this challenge, our study introduces an innovative cross-modal TL strategy. By compressing ImageNet parameters, which has allowed us to transfer the robust capabilities of 2D models into the 1D domain. This was achieved by employing various DL architectures, thereby validating the robustness of our method. Furthermore, by introducing the hyperparameter a and compressing the weight magnitude in TL, we further investigated our proposed method to enhance its universality. This is evidenced by the positive results obtained from HAR benchmark datasets. We also expanded our analysis to include results from different sensor types, affirming the adaptability and effectiveness of our TL strategy across diverse contexts. These experiments have not only reinforced our initial findings but have also widened the understanding of how our approach can be applied to a vast array of IoT-based scenarios and sensor data, opening up new avenues for research and application in the field.