Abstract
Building activity recognition systems conventionally involves training a common model from all data of training users and utilizing this model to recognize activities of unseen subjects. However, participants come from diverse demographics, so that different users can perform the same actions in diverse ways. Each subject might exhibit user-specific signal patterns, yet a group of users may perform activities in similar manners and share analogous patterns. Leveraging this intuition, we explore Frechet Inception Distance (FID) as a distribution matching-based metric to measure the similarity between users. From that, we propose the nearest-FID-neighbors and the FID-graph clustering techniques to develop user-specific models that are trained with data from the community the testing user likely belongs to. Verified on a series of benchmark wearable datasets, the proposed techniques significantly outperform the model trained with all users.
H. T. Nguyen and H. Kwon—Both authors contributed equally to this research.
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Nguyen, H.T., Kwon, H., Haresamudram, H., Peterson, A.F., Plötz, T. (2021). Personalization Models for Human Activity Recognition with Distribution Matching-Based Metrics. In: Li, X., Wu, M., Chen, Z., Zhang, L. (eds) Deep Learning for Human Activity Recognition. DL-HAR 2021. Communications in Computer and Information Science, vol 1370. Springer, Singapore. https://doi.org/10.1007/978-981-16-0575-8_4
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