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Sensors 2018, 18(5), 1654; https://doi.org/10.3390/s18051654

Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry

1
Department of Computer Science, Amsterdam University of Applied Sciences, 1091 GM Amsterdam, The Netherlands
2
Human Media Interaction, University of Twente, 7522 NH Enschede, The Netherlands
3
Neuroscience Research Australia, University of New South Wales, Sydney 2031, Australia
4
Department of Human Movement Sciences, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
5
Informatics Institute, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
*
Author to whom correspondence should be addressed.
Received: 31 March 2018 / Revised: 13 May 2018 / Accepted: 18 May 2018 / Published: 22 May 2018
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Abstract

Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment of fall risk. Body-worn sensors such as accelerometers can provide valuable insight into fall risk. Currently, biomechanical features derived from accelerometer data are used for the assessment of fall risk. Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk. We used an existing dataset of 296 older adults. We compared the performance of three deep learning model architectures (convolutional neural network (CNN), long short-term memory (LSTM) and a combination of these two (ConvLSTM)) to each other and to a baseline model with biomechanical features on the same dataset. The results show that the deep learning models in a single-task learning mode are strong in recognition of identity of the subject, but that these models only slightly outperform the baseline method on fall risk assessment. When using multi-task learning, with gender and age as auxiliary tasks, deep learning models perform better. We also found that preprocessing of the data resulted in the best performance (AUC = 0.75). We conclude that deep learning models, and in particular multi-task learning, effectively assess fall risk on the basis of wearable sensor data. View Full-Text
Keywords: accidental falls; older adults; machine learning; neural networks; convolutional neural network; long short-term memory; accelerometry accidental falls; older adults; machine learning; neural networks; convolutional neural network; long short-term memory; accelerometry
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Nait Aicha, A.; Englebienne, G.; van Schooten, K.S.; Pijnappels, M.; Kröse, B. Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry. Sensors 2018, 18, 1654.

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