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.
Highlights
Falls among older adults are one of the major health problems that lead to a decreased quality of life and increased morbidity and mortality
We looked at the long short-term memory (LSTM) model, a specific type of recurrent neural network (RNN)
We compared deep neural networks (DNNs) with the current state-of-the-art model described in Section 5.1, which relies on manually engineered feature extraction
Summary
Falls among older adults are one of the major health problems that lead to a decreased quality of life and increased morbidity and mortality. Falls pose high costs to the public health service. Risk factors for falls include weak muscles, unsteady gait, cognitive decline, and psychoactive medications. Detection and monitoring of fall risk factors can significantly reduce the risk of future falls [1,2]. Among these factors, history of falls and of gait and balance disorders have been identified as strong predictors [3]. Fall risk assessment is a process in which the probability of a future fall is estimated, usually within a time frame of 6–12 months. In many intervention programs proposed for fall prevention, Sensors 2018, 18, 1654; doi:10.3390/s18051654 www.mdpi.com/journal/sensors
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