Systematic Evaluation of Self-Supervised Learning Approaches for Wearable-Based Fatigue Recognition

Tamás Visy, Rita Kuznetsova, Christian Holz, Shkurta Gashi
Proceedings of the fifth Conference on Health, Inference, and Learning, PMLR 248:582-596, 2024.

Abstract

Fatigue is one of the most prevalent symptoms of chronic diseases, such as Multiple Sclerosis, Alzheimer’s, and Parkinson’s. Recently researchers have explored unobtrusive and continuous ways of fatigue monitoring using mobile and wearable devices. However, data quality and limited labeled data availability in the wearable health domain pose significant challenges to progress in the field. In this work, we perform a systematic evaluation of self-supervised learning (SSL) tasks for fatigue recognition using wearable sensor data. To establish our benchmark, we use Homekit2020, which is a large-scale dataset collected using Fitbit devices in everyday life settings. Our results show that the majority of the SSL tasks outperform fully supervised baselines for fatigue recognition, even in limited labeled data scenarios. In particular, the domain features and multi-task learning achieve 0.7371 and 0.7323 AUROC, which are higher than the other SSL tasks and supervised learning baselines. In most of the pre-training tasks, the performance is higher when using at least one data augmentation that reflects the potentially low quality of wearable data (e.g., missing data). Our findings open up promising opportunities for continuous assessment of fatigue in real settings and can be used to guide the design and development of health monitoring systems.

Cite this Paper

BibTeX
@InProceedings{pmlr-v248-visy24a, title = {Systematic Evaluation of Self-Supervised Learning Approaches for Wearable-Based Fatigue Recognition}, author = {Visy, Tam\'as and Kuznetsova, Rita and Holz, Christian and Gashi, Shkurta}, booktitle = {Proceedings of the fifth Conference on Health, Inference, and Learning}, pages = {582--596}, 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/visy24a/visy24a.pdf}, url = {https://proceedings.mlr.press/v248/visy24a.html}, abstract = {Fatigue is one of the most prevalent symptoms of chronic diseases, such as Multiple Sclerosis, Alzheimer’s, and Parkinson’s. Recently researchers have explored unobtrusive and continuous ways of fatigue monitoring using mobile and wearable devices. However, data quality and limited labeled data availability in the wearable health domain pose significant challenges to progress in the field. In this work, we perform a systematic evaluation of self-supervised learning (SSL) tasks for fatigue recognition using wearable sensor data. To establish our benchmark, we use Homekit2020, which is a large-scale dataset collected using Fitbit devices in everyday life settings. Our results show that the majority of the SSL tasks outperform fully supervised baselines for fatigue recognition, even in limited labeled data scenarios. In particular, the domain features and multi-task learning achieve 0.7371 and 0.7323 AUROC, which are higher than the other SSL tasks and supervised learning baselines. In most of the pre-training tasks, the performance is higher when using at least one data augmentation that reflects the potentially low quality of wearable data (e.g., missing data). Our findings open up promising opportunities for continuous assessment of fatigue in real settings and can be used to guide the design and development of health monitoring systems.} }
Endnote
%0 Conference Paper %T Systematic Evaluation of Self-Supervised Learning Approaches for Wearable-Based Fatigue Recognition %A Tamás Visy %A Rita Kuznetsova %A Christian Holz %A Shkurta Gashi %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-visy24a %I PMLR %P 582--596 %U https://proceedings.mlr.press/v248/visy24a.html %V 248 %X Fatigue is one of the most prevalent symptoms of chronic diseases, such as Multiple Sclerosis, Alzheimer’s, and Parkinson’s. Recently researchers have explored unobtrusive and continuous ways of fatigue monitoring using mobile and wearable devices. However, data quality and limited labeled data availability in the wearable health domain pose significant challenges to progress in the field. In this work, we perform a systematic evaluation of self-supervised learning (SSL) tasks for fatigue recognition using wearable sensor data. To establish our benchmark, we use Homekit2020, which is a large-scale dataset collected using Fitbit devices in everyday life settings. Our results show that the majority of the SSL tasks outperform fully supervised baselines for fatigue recognition, even in limited labeled data scenarios. In particular, the domain features and multi-task learning achieve 0.7371 and 0.7323 AUROC, which are higher than the other SSL tasks and supervised learning baselines. In most of the pre-training tasks, the performance is higher when using at least one data augmentation that reflects the potentially low quality of wearable data (e.g., missing data). Our findings open up promising opportunities for continuous assessment of fatigue in real settings and can be used to guide the design and development of health monitoring systems.
APA
Visy, T., Kuznetsova, R., Holz, C. & Gashi, S.. (2024). Systematic Evaluation of Self-Supervised Learning Approaches for Wearable-Based Fatigue Recognition. Proceedings of the fifth Conference on Health, Inference, and Learning, in Proceedings of Machine Learning Research 248:582-596 Available from https://proceedings.mlr.press/v248/visy24a.html.

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