research-article

METIER: A Deep Multi-Task Learning Based Activity and User Recognition Model Using Wearable Sensors

Authors:

Liangying PengAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 4, Issue 1

Article No.: 5, Pages 1 - 18

https://doi.org/10.1145/3381012

Published: 18 March 2020 Publication History

Abstract

Activity recognition (AR) and user recognition (UR) using wearable sensors are two key tasks in ubiquitous and mobile computing. Currently, they still face some challenging problems. For one thing, due to the variations in how users perform activities, the performance of a well-trained AR model typically drops on new users. For another, existing UR models are powerless to activity changes, as there are significant differences between the sensor data in different activity scenarios. To address these problems, we propose METIER (deep multi-task learning based activity and user recognition) model, which solves AR and UR tasks jointly and transfers knowledge across them. User-related knowledge from UR task helps AR task to model user characteristics, and activity-related knowledge from AR task guides UR task to handle activity changes. METIER softly shares parameters between AR and UR networks, and optimizes these two networks jointly. The commonalities and differences across tasks are exploited to promote AR and UR tasks simultaneously. Furthermore, mutual attention mechanism is introduced to enable AR and UR tasks to exploit their knowledge to highlight important features for each other. Experiments are conducted on three public datasets, and the results show that our model can achieve competitive performance on both tasks.

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 4, Issue 1

March 2020

1006 pages

EISSN:2474-9567

DOI:10.1145/3388993

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Copyright © 2020 ACM.

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Publication History

Published: 18 March 2020

Published in IMWUT Volume 4, Issue 1

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Cited By

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https://doi.org/10.1109/TNNLS.2023.3330879
Bock MMoeller MVan Laerhoven K(2024)Temporal Action Localization for Inertial-based Human Activity RecognitionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997708:4(1-19)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699770
Hong ZLi ZZhong SLyu WWang HDing YHe TZhang D(2024)CrossHAR: Generalizing Cross-dataset Human Activity Recognition via Hierarchical Self-Supervised PretrainingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595978:2(1-26)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659597
Ge WMou GAgu ELee K(2024)Deep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity RecognitionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314447:4(1-23)Online publication date: 12-Jan-2024
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Miao SChen LHu R(2024)Spatial-Temporal Masked Autoencoder for Multi-Device Wearable Human Activity RecognitionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314157:4(1-25)Online publication date: 12-Jan-2024
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