Daily Physical Activity Monitoring: Adaptive Learning from Multi-source Motion Sensor Data

Haoting Zhang, Donglin Zhan, Yunduan Lin, Jinghai He, Qing Zhu, Zuo-Jun Shen, Zeyu Zheng
Proceedings of the fifth Conference on Health, Inference, and Learning, PMLR 248:39-54, 2024.

Abstract

In healthcare applications, there is a growing need to develop machine learning models that use data from a single source, such as that from a wrist wearable device, to monitor physical activities, assess health risks, and provide immediate health recommendations or interventions. However, the limitation of using single-source data often compromises the model’s accuracy, as it fails to capture the full scope of human activities. While a more comprehensive dataset can be gathered in a lab setting using multiple sensors attached to various body parts, this approach is not practical for everyday use due to the impracticality of wearing multiple sensors. To address this challenge, we introduce a transfer learning framework that optimizes machine learning models for everyday applications by leveraging multi-source data collected in a laboratory setting. We introduce a novel metric to leverage the inherent relationship between these multiple data sources, as they are all paired to capture aspects of the same physical activity. Through numerical experiments, our framework outperforms existing methods in classification accuracy and robustness to noise, offering a promising avenue for the enhancement of daily activity monitoring.

Cite this Paper

BibTeX
@InProceedings{pmlr-v248-zhang24a, title = {Daily Physical Activity Monitoring: Adaptive Learning from Multi-source Motion Sensor Data}, author = {Zhang, Haoting and Zhan, Donglin and Lin, Yunduan and He, Jinghai and Zhu, Qing and Shen, Zuo-Jun and Zheng, Zeyu}, booktitle = {Proceedings of the fifth Conference on Health, Inference, and Learning}, pages = {39--54}, year = {2024}, editor = {Pollard, Tom and Choi, Edward and Singhal, Pankhuri and Hughes, Michael and Sizikova, Elena and Mortazavi, Bobak and Chen, Irene and Wang, Fei and Sarker, Tasmie and McDermott, Matthew and Ghassemi, Marzyeh}, volume = {248}, series = {Proceedings of Machine Learning Research}, month = {27--28 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v248/main/assets/zhang24a/zhang24a.pdf}, url = {https://proceedings.mlr.press/v248/zhang24a.html}, abstract = {In healthcare applications, there is a growing need to develop machine learning models that use data from a single source, such as that from a wrist wearable device, to monitor physical activities, assess health risks, and provide immediate health recommendations or interventions. However, the limitation of using single-source data often compromises the model’s accuracy, as it fails to capture the full scope of human activities. While a more comprehensive dataset can be gathered in a lab setting using multiple sensors attached to various body parts, this approach is not practical for everyday use due to the impracticality of wearing multiple sensors. To address this challenge, we introduce a transfer learning framework that optimizes machine learning models for everyday applications by leveraging multi-source data collected in a laboratory setting. We introduce a novel metric to leverage the inherent relationship between these multiple data sources, as they are all paired to capture aspects of the same physical activity. Through numerical experiments, our framework outperforms existing methods in classification accuracy and robustness to noise, offering a promising avenue for the enhancement of daily activity monitoring.} }
Endnote
%0 Conference Paper %T Daily Physical Activity Monitoring: Adaptive Learning from Multi-source Motion Sensor Data %A Haoting Zhang %A Donglin Zhan %A Yunduan Lin %A Jinghai He %A Qing Zhu %A Zuo-Jun Shen %A Zeyu Zheng %B Proceedings of the fifth Conference on Health, Inference, and Learning %C Proceedings of Machine Learning Research %D 2024 %E Tom Pollard %E Edward Choi %E Pankhuri Singhal %E Michael Hughes %E Elena Sizikova %E Bobak Mortazavi %E Irene Chen %E Fei Wang %E Tasmie Sarker %E Matthew McDermott %E Marzyeh Ghassemi %F pmlr-v248-zhang24a %I PMLR %P 39--54 %U https://proceedings.mlr.press/v248/zhang24a.html %V 248 %X In healthcare applications, there is a growing need to develop machine learning models that use data from a single source, such as that from a wrist wearable device, to monitor physical activities, assess health risks, and provide immediate health recommendations or interventions. However, the limitation of using single-source data often compromises the model’s accuracy, as it fails to capture the full scope of human activities. While a more comprehensive dataset can be gathered in a lab setting using multiple sensors attached to various body parts, this approach is not practical for everyday use due to the impracticality of wearing multiple sensors. To address this challenge, we introduce a transfer learning framework that optimizes machine learning models for everyday applications by leveraging multi-source data collected in a laboratory setting. We introduce a novel metric to leverage the inherent relationship between these multiple data sources, as they are all paired to capture aspects of the same physical activity. Through numerical experiments, our framework outperforms existing methods in classification accuracy and robustness to noise, offering a promising avenue for the enhancement of daily activity monitoring.
APA
Zhang, H., Zhan, D., Lin, Y., He, J., Zhu, Q., Shen, Z. & Zheng, Z.. (2024). Daily Physical Activity Monitoring: Adaptive Learning from Multi-source Motion Sensor Data. Proceedings of the fifth Conference on Health, Inference, and Learning, in Proceedings of Machine Learning Research 248:39-54 Available from https://proceedings.mlr.press/v248/zhang24a.html.

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