Comparison of machine learning approaches for near-fall-detection with motion sensors

Frontiers in Digital Health

Digital Health

Elias Krey¹  Andreas Hein¹  Sandra Hellmers¹  Tim Stuckenschneider²  Laura Schmidt²  Jessica Koschate²  Arber Gashi²  Tania Zieschang² 

[1] Assistance Systems and Medical Device Technology, Department for Health Services Research, Carl von Ossietzky University, Oldenburg, Germany;Geriatric Medicine, Department for Health Services Research, Carl von Ossietzky University, Oldenburg, Germany;

关键词: near-fall;    perturbation;    CNN;    machine learning;    IMU;    fall risk;    mobile health;   

DOI  :  10.3389/fdgth.2023.1223845

 received in 2023-05-16, accepted in 2023-07-06,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionFalls are one of the most common causes of emergency hospital visits in older people. Early recognition of an increased fall risk, which can be indicated by the occurrence of near-falls, is important to initiate interventions.MethodsIn a study with 87 subjects we simulated near-fall events on a perturbation treadmill and recorded them with inertial measurement units (IMU) at seven different positions. We investigated different machine learning models for the near-fall detection including support vector machines, AdaBoost, convolutional neural networks, and bidirectional long short-term memory networks. Additionally, we analyzed the influence of the sensor position on the classification results.ResultsThe best results showed a DeepConvLSTM with an F1 score of 0.954 (precision 0.969, recall 0.942) at the sensor position “left wrist.”DiscussionSince these results were obtained in the laboratory, the next step is to evaluate the suitability of the classifiers in the field.

【 授权许可】

Unknown   
© 2023 Hellmers, Krey, Gashi, Koschate, Schmidt, Stuckenschneider, Hein and Zieschang.