Deploying Machine Learning in Resource-Constrained Devices for Human Activity Recognition

• Rafael Schild Reusch PUCRS
• Leonardo Rezende Juracy PUCRS
• Fernando Gehm Moraes PUCRS

Resumo

Machine Learning (ML) has proven to be highly effective in solving complex tasks such as human activity and speech recognition. However, the introduction of accuracy-driven ML models has brought new challenges in terms of their applicability in resource-constrained systems. In Human Activity Recognition (HAR), current state-of-the-art approaches often rely on complex multilayer LSTM (Long Short Term Memory) networks once they are well suited to handle temporal series data, a crucial aspect of HAR, but presenting a high computational cost associated with running the inference phase. In HAR, low-power IoT devices, such as wearable sensor arrays, are frequently used as data-gathering devices. However, we observed a limited effort to deploy ML technology directly on these devices, most commonly using edge or cloud computing services, which can be unavailable in some situations. This work aims to provide a Convolutional Neural Network (CNN) tuned for resource-constrained embedded systems. After tuning the CNN model in the Pytorch framework, we present an equivalent C model and employ optimization techniques. The results show that, compared to the reference CNN, the optimized model reduced the CNN model 2.34 times, does not require floating-point units (FPUs), and improved accuracy from 74.9% to 85.2%. These results show the feasibility of running the proposed CNN on resource-constrained devices.

Palavras-chave: Machine Learning, 1D CNN, Human Activity Recognition, Embedded Systems, Constrained Devices