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

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. Bod...

詳細記述

保存先:
書誌詳細
主要な著者: Ahmed Nait Aicha, Gwenn Englebienne, Kimberley S. van Schooten, Mirjam Pijnappels, Ben Kröse
フォーマット: Artigo
言語:Inglês
出版事項: MDPI AG 2018-05-01
シリーズ:Sensors
主題:
accidental falls
older adults
machine learning
neural networks
convolutional neural network
long short-term memory
accelerometry
オンライン･アクセス:http://www.mdpi.com/1424-8220/18/5/1654
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