Konstantina Pantelidou
ABSTRACT
Today people live with a variety of diseases and health conditions that can impact their daily lives, from chronic illnesses such as cancer to mental health issues such as anxiety and depression. With the advancement of technology and healthcare, quality of life (QoL) prediction and improvement is becoming increasingly important to help individuals live better. In addition to data collected by questionnaires, machine learning can be employed to predict aspects of QoL, such as fatigue and anxiety, based on data from wearable devices. However, this requires ground truth information, which, in real-world cases is not always present, or is scarce. This paper addresses the problem of fatigue prediction, which is part of the overall QoL prediction. We extend previous literature by viewing the problem as regression, with the ultimate goal of transferring a model trained with a complete dataset of the literature to a real-world dataset with no ground truth, facing the challenge of only a few common features. We compare several regression and classification techniques to select the best-performing ones. We then explore the effect of using only limited important features on accuracy, which allow us to assess the applicability of transferring the models to a real-world dataset. We apply the trained model on data from the ONCORELIEF project and discuss the identified challenges.
- Pramod Bobade and M Vani. 2020. Stress detection with machine learning and deep learning using multimodal physiological data. In 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 51–57.Google Scholar
Cross Ref
- Leo Breiman. 2001. Random forests. Machine learning 45 (2001), 5–32.Google Scholar
- Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 785–794.Google ScholarDigital Library
- Jerome H Friedman. 2001. Greedy function approximation: a gradient boosting machine. Annals of statistics (2001), 1189–1232.Google Scholar
- Asma Ghandeharioun, Szymon Fedor, Lisa Sangermano, Dawn Ionescu, Jonathan Alpert, Chelsea Dale, David Sontag, and Rosalind Picard. 2017. Objective assessment of depressive symptoms with machine learning and wearable sensors data. In 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII). 325–332. https://doi.org/10.1109/ACII.2017.8273620Google ScholarCross Ref
- Klaus Greff, Rupesh K Srivastava, Jan Koutník, Bas R Steunebrink, and Jürgen Schmidhuber. 2016. LSTM: A search space odyssey. IEEE transactions on neural networks and learning systems 28, 10 (2016), 2222–2232.Google Scholar
- H2020. 2023. European project ONCORELIEF. https://oncorelief.eu/.Google Scholar
- Arthur E Hoerl and Robert W Kennard. 1970. Ridge regression: applications to nonorthogonal problems. Technometrics 12, 1 (1970), 69–82.Google ScholarCross Ref
- Jr Cristovão Iglesias, Varun Mehta, Alina Venereo-Sanchez, Xingge Xu, Julien Robitaille, Robert Voyer, René Richard, Nabil Belacel, Amine Kamen, and Miodrag Bolic. 2021. Handling Massive Proportion of Missing Labels in Multivariate Long-Term Time Series Forecasting. In Journal of Physics: Conference Series, Vol. 2090. IOP Publishing, 012170.Google Scholar
- Mikhail Kamalov, Aurélie Boisbunon, Carlo Fanara, Ingrid Grenet, and Jonathan Daeden. 2021. PaZoe: classifying time series with few labels. In 2021 29th European Signal Processing Conference (EUSIPCO). IEEE, 1561–1565.Google ScholarCross Ref
- Hongyu Luo, Pierre-Alexandre Lee, Ieuan Clay, Martin Jaggi, and Valeria De Luca. 2020. Assessment of fatigue using wearable sensors: a pilot study. Digital biomarkers 4, 1 (2020), 59–72.Google Scholar
- Lukas Meier, Sara Van De Geer, and Peter Bühlmann. 2008. The group lasso for logistic regression. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 70, 1 (2008), 53–71.Google ScholarCross Ref
- Anthony J Myles, Robert N Feudale, Yang Liu, Nathaniel A Woody, and Steven D Brown. 2004. An introduction to decision tree modeling. Journal of Chemometrics: A Journal of the Chemometrics Society 18, 6 (2004), 275–285.Google ScholarCross Ref
- Joanne Peng, Kuk Lee, and Gary Ingersoll. 2002. An Introduction to Logistic Regression Analysis and Reporting. Journal of Educational Research - J EDUC RES 96 (09 2002), 3–14. https://doi.org/10.1080/00220670209598786Google ScholarCross Ref
- Christian Schuldt, Ivan Laptev, and Barbara Caputo. 2004. Recognizing human actions: a local SVM approach. In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., Vol. 3. IEEE, 32–36.Google ScholarCross Ref
- George AF Seber and Alan J Lee. 2003. Linear regression analysis. Vol. 330. John Wiley & Sons.Google Scholar
- Gabriel Tseng, Hannah Kerner, Catherine Nakalembe, and Inbal Becker-Reshef. 2021. Learning To Predict Crop Type From Heterogeneous Sparse Labels Using Meta-Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. 1111–1120.Google ScholarCross Ref
Index Terms
- Towards prediction of Quality of Life aspects using wearable data with limited ground truth
Recommendations
Elderly Assistance Using Wearable Sensors by Detecting Fall and Recognizing Fall Patterns
UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable ComputersFalling is a serious threat to the elderly people. One severe fall can cause hazardous problems like bone fracture or may lead to some permanent disability or even death. Thus, it has become the need of the hour to continuously monitor the activities of ...
A survey on wearable sensor modality centred human activity recognition in health care
Highlights- Discuss limitations and strengths of different sensor modalities.
- Focus on the ...
AbstractIncreased life expectancy coupled with declining birth rates is leading to an aging population structure. Aging-caused changes, such as physical or cognitive decline, could affect people's quality of life, result in injuries, mental ...
Stress Prediction Using Per-Activity Biometric Data to Improve QoL in the Elderly
Participative Urban Health and Healthy Aging in the Age of AIAbstractTo improve the QoL of the elderly, it is essential to predict their stress states. In general, the stress state varies from day to day or time to time depending on what activities are performed and how long/strong. However, most existing studies ...
Comments