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Beschreibung:
Real-time monitoring of human activities using wearable devices often requires the deployment of machine learning models on resource-constrained edge devices. State-of- the-art Human Activity Recognition models suffer from excessive size and complexity. Furthermore, our systematic analysis reveals that even worse, the computational cost and model size of most SOTA HAR models escalate significantly with increasing sensor channels. With advances in sensor technology that make it easier to scale sensor deployments that capture human activities, ad- dressing this challenge becomes critical for practical applicability. In this work, we propose an integrated neural architecture search framework to further lighten HAR models. The proposed framework simultaneously selects and reduces the number of sensor channels, prunes filters, and decreases the temporal dimensions while training the model on optimized hardware. This results in smaller and less complex models. Experiments on three HAR datasets demonstrate that our framework outperforms two state-of-the-art pruning methods in reducing model size and com- plexity, while achieving superior performance. Furthermore, we successfully applied our proposed framework to the deployment of a HAR model on a microcontroller, highlighting its feasibility for real-world implementation.