MLP-HAR: Boosting Performance and Efficiency of HAR Models on Edge Devices with Purely Fully Connected Layers

Neural network models have demonstrated exceptional performance in wearable human activity recognition (HAR) tasks. However, the increasing size or complexity of HAR models significantly impacts their deployment on wearable devices with limited computational power. In this study, we introduce a novel HAR model architecture named Multi-Layer Perceptron-HAR (MLP-HAR), which contains solely fully connected layers. This model is specifically designed to address the unique characteristics of HAR tasks, such as multi-modality interaction and global temporal information. The MLP-HAR model employs fully connected layers that alternately operate along the modality and temporal dimensions, enabling multiple fusions of information across these dimensions. Our proposed model demonstrates comparable performance with other state-of-the-art HAR models on six open-source datasets, while utilizing significantly fewer learnable parameters and exhibiting lower model complexity. Specifically, the complexity of our model is at least ten times smaller than that of the TinyHAR model and several hundred times smaller than the benchmark model DeepConvLSTM. Additionally, due to its purely fully connected layer-based architecture, MLP-HAR offers the advantage of ease of deployment. To substantiate these claims, we report the inference time performance of MLP-HAR on the Samsung Galaxy Watch 5 PRO and the Arduino Portenta H7 LITE, comparing it against other state-of-the-art HAR models.