@inproceedings{8bc7108f22bd48768c4a6504028ba29f,
title = "Motion sensor-based assessment on fall risk and Parkinson's disease severity: A deep multi-source multi-task learning (DMML) Approach",
abstract = "Senior citizens face many challenges to their independent living. Fall and Parkinson's disease (PD) are among the most life-threatening events or conditions. Precise and prompt motion sensor-based assessments for fall risk and PD severity can lead to timely interventions that can relieve or ultimately eliminate the threats. However, past studies focus on ad hoc feature engineering, which is subjective and less transferrable to related domains. In this work, we propose a Deep Multi-source Multi-task Learning (DMML) approach that provides an integrated framework for sensor-based condition risk and severity assessment. We develop Convolutional Neural Networks (CNN) to extract features from sensor signals. We collect timed up and go (TUG) test data at a neurology clinic to evaluate our model. Five sensors are attached to 22 PD patients to collect motion data throughout the test. Our model achieves F-measure of 0.940 for assessing fall risks, and RMSE of 0.060 for assessing PD severities, significantly outperforming the benchmark methods.",
keywords = "Convolutional neural networks, Deep learning, Fall, Motion sensors, Multi-task learning, Parkinson's disease, Risk assessment",
author = "Shuo Yu and Hsinchun Chen and Randall Brown and Scott Sherman",
note = "Funding Information: This study is part of the SilverLink project funded by the National Science Foundation and run by the Artificial Intelligence Lab at the University of Arizona. [33] We have developed sensors and gateways for collecting data from the human body and the environment. Accelerometers, gyroscopes, and thermometers are embedded in the sensors (inertial unit model: Bosch BMI160). The shape and size of the sensor can be found in Figure 3a. In this study, we only utilize the accelerometers for our analysis. Funding Information: This study was supported by USA NSF STTR-1622788. Publisher Copyright: {\textcopyright} 2018 IEEE.; null ; Conference date: 04-06-2018 Through 07-06-2018",
year = "2018",
month = jul,
day = "24",
doi = "10.1109/ICHI.2018.00027",
language = "English",
series = "Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "174--179",
booktitle = "Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018",
}