Sensors, Vol. 18, Pages 1654: Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry

Sensors, Vol. 18, Pages 1654: Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry Sensors doi: 10.3390/s18051654 Authors: Ahmed Nait Aicha Gwenn Englebienne Kimberley S. van Schooten Mirjam Pijnappels Ben Kröse 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 gend...
Source: Sensors - Category: Biotechnology Authors: Tags: Review Source Type: research