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Cross-Domain Activity Recognition Using Shared Representation in Sensor Data

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Cross-Domain Activity Recognition Using Shared Representation in Sensor Data. / Hamad, Rebeen Ali; Yang, Longzhi; Woo, Wai Lok et al.
In: IEEE Sensors Journal, Vol. 22, No. 13, 01.07.2022, p. 13273-13284.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Hamad, RA, Yang, L, Woo, WL & Wei, B 2022, 'Cross-Domain Activity Recognition Using Shared Representation in Sensor Data', IEEE Sensors Journal, vol. 22, no. 13, pp. 13273-13284. https://doi.org/10.1109/jsen.2022.3178083

APA

Hamad, R. A., Yang, L., Woo, W. L., & Wei, B. (2022). Cross-Domain Activity Recognition Using Shared Representation in Sensor Data. IEEE Sensors Journal, 22(13), 13273-13284. https://doi.org/10.1109/jsen.2022.3178083

Vancouver

Hamad RA, Yang L, Woo WL, Wei B. Cross-Domain Activity Recognition Using Shared Representation in Sensor Data. IEEE Sensors Journal. 2022 Jul 1;22(13):13273-13284. Epub 2022 Jun 2. doi: 10.1109/jsen.2022.3178083

Author

Hamad, Rebeen Ali ; Yang, Longzhi ; Woo, Wai Lok et al. / Cross-Domain Activity Recognition Using Shared Representation in Sensor Data. In: IEEE Sensors Journal. 2022 ; Vol. 22, No. 13. pp. 13273-13284.

Bibtex

@article{55be465a012d4a54b58912d982c78137,
title = "Cross-Domain Activity Recognition Using Shared Representation in Sensor Data",
abstract = "Existing models based on sensor data for human activity recognition are reporting state-of-the-art performances. Most of these models are conducted based on single-domain learning in which for each domain a model is required to be trained. However, the generation of adequate labelled data and a learning model for each domain separately is often time-consuming and computationally expensive. Moreover, the deployment of multiple domain-wise models is not scalable as it obscures domain distinctions, introduces extra computational costs, and limits the usefulness of training data. To mitigate this, we propose a multi-domain learning network to transfer knowledge across different but related domains and alleviate isolated learning paradigms using a shared representation. The proposed network consists of two identical causal convolutional sub-networks that are projected to a shared representation followed by a linear attention mechanism. The proposed network can be trained using the full training dataset of the source domain and a dataset of restricted size of the target training domain to reduce the need of large labelled training datasets. The network processes the source and target domains jointly to learn powerful and mutually complementary features to boost the performance in both domains. The proposed multi-domain learning network on six real-world sensor activity datasets outperforms the existing methods by applying only 50% of the labelled data. This confirms the efficacy of the proposed approach as a generic model to learn human activities from different but related domains in a joint effort, to reduce the number of required models and thus improve system efficiency.",
keywords = "Activity Recognition, Activity recognition, Cross-domain learning, Data models, Deep Learning, Deep learning, Sensor Data, Temporal Evaluation, Training, Training data, Transfer learning, Wearable sensors",
author = "Hamad, {Rebeen Ali} and Longzhi Yang and Woo, {Wai Lok} and Bo Wei",
note = "{\textcopyright}2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2022",
month = jul,
day = "1",
doi = "10.1109/jsen.2022.3178083",
language = "English",
volume = "22",
pages = "13273--13284",
journal = "IEEE Sensors Journal",
issn = "1530-437X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "13",

}

RIS

TY - JOUR

T1 - Cross-Domain Activity Recognition Using Shared Representation in Sensor Data

AU - Hamad, Rebeen Ali

AU - Yang, Longzhi

AU - Woo, Wai Lok

AU - Wei, Bo

N1 - ©2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2022/7/1

Y1 - 2022/7/1

N2 - Existing models based on sensor data for human activity recognition are reporting state-of-the-art performances. Most of these models are conducted based on single-domain learning in which for each domain a model is required to be trained. However, the generation of adequate labelled data and a learning model for each domain separately is often time-consuming and computationally expensive. Moreover, the deployment of multiple domain-wise models is not scalable as it obscures domain distinctions, introduces extra computational costs, and limits the usefulness of training data. To mitigate this, we propose a multi-domain learning network to transfer knowledge across different but related domains and alleviate isolated learning paradigms using a shared representation. The proposed network consists of two identical causal convolutional sub-networks that are projected to a shared representation followed by a linear attention mechanism. The proposed network can be trained using the full training dataset of the source domain and a dataset of restricted size of the target training domain to reduce the need of large labelled training datasets. The network processes the source and target domains jointly to learn powerful and mutually complementary features to boost the performance in both domains. The proposed multi-domain learning network on six real-world sensor activity datasets outperforms the existing methods by applying only 50% of the labelled data. This confirms the efficacy of the proposed approach as a generic model to learn human activities from different but related domains in a joint effort, to reduce the number of required models and thus improve system efficiency.

AB - Existing models based on sensor data for human activity recognition are reporting state-of-the-art performances. Most of these models are conducted based on single-domain learning in which for each domain a model is required to be trained. However, the generation of adequate labelled data and a learning model for each domain separately is often time-consuming and computationally expensive. Moreover, the deployment of multiple domain-wise models is not scalable as it obscures domain distinctions, introduces extra computational costs, and limits the usefulness of training data. To mitigate this, we propose a multi-domain learning network to transfer knowledge across different but related domains and alleviate isolated learning paradigms using a shared representation. The proposed network consists of two identical causal convolutional sub-networks that are projected to a shared representation followed by a linear attention mechanism. The proposed network can be trained using the full training dataset of the source domain and a dataset of restricted size of the target training domain to reduce the need of large labelled training datasets. The network processes the source and target domains jointly to learn powerful and mutually complementary features to boost the performance in both domains. The proposed multi-domain learning network on six real-world sensor activity datasets outperforms the existing methods by applying only 50% of the labelled data. This confirms the efficacy of the proposed approach as a generic model to learn human activities from different but related domains in a joint effort, to reduce the number of required models and thus improve system efficiency.

KW - Activity Recognition

KW - Activity recognition

KW - Cross-domain learning

KW - Data models

KW - Deep Learning

KW - Deep learning

KW - Sensor Data

KW - Temporal Evaluation

KW - Training

KW - Training data

KW - Transfer learning

KW - Wearable sensors

U2 - 10.1109/jsen.2022.3178083

DO - 10.1109/jsen.2022.3178083

M3 - Journal article

VL - 22

SP - 13273

EP - 13284

JO - IEEE Sensors Journal

JF - IEEE Sensors Journal

SN - 1530-437X

IS - 13

ER -