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2021 | OriginalPaper | Buchkapitel

Personalization Models for Human Activity Recognition with Distribution Matching-Based Metrics

verfasst von : Huy Thong Nguyen, Hyeokhyen Kwon, Harish Haresamudram, Andrew F. Peterson, Thomas Plötz

Erschienen in: Deep Learning for Human Activity Recognition

Verlag: Springer Singapore

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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.

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Vorheriges Kapitel Toward Data Augmentation and Interpretation in Sensor-Based Fine-Grained Hand Activity Recognition

Nächstes Kapitel Resource-Constrained Federated Learning with Heterogeneous Labels and Models for Human Activity Recognition

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Titel: Personalization Models for Human Activity Recognition with Distribution Matching-Based Metrics
verfasst von: Huy Thong Nguyen
Hyeokhyen Kwon
Harish Haresamudram
Andrew F. Peterson
Thomas Plötz
Verlag: Springer Singapore
Buch: Deep Learning for Human Activity Recognition
Print ISBN: 978-981-16-0574-1

Electronic ISBN: 978-981-16-0575-8

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-981-16-0575-8_4

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