preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202309.0635.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 7 September 2023 / Approved: 11 September 2023 / Online: 11 September 2023 (05:30:21 CEST)

Convolution, recurrent and attention-based deep learning techniques have produced the most recent state-of-the-art results in multiple sensor-based human activity recognition (HAR) datasets. However, these techniques have high computing costs, restricting their use in low-powered devices. Different methods have been employed to increase the efficiency of these techniques; however, this often results in worse performance. Recently, pure MLP architectures have demonstrated competitive performance in vision-based tasks with lower computation costs than other deep-learning techniques. The MLP-Mixer is a pioneering pure MLP architecture that produces competitive results with state-of-the-art models in computer vision tasks. This paper shows the viability of the MLP-Mixer in sensor-based HAR. Furthermore, experiments are performed to gain insight into the Mixer modules essential for HAR, and a visual analysis of the Mixer’s weights is provided, validating the Mixer’s learning capabilities. As a result, the Mixer achieves an F1 score of 97%, 84.2%, 91.2% and 90% on the PAMAP2, Daphnet Gait, Opportunity Gestures and Opportunity Locomotion datasets, respectively, outperforming state-of-the-art models in all datasets except Opportunity Gestures.

human activity recognition; MLP-Mixer; efficiency

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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