Citation

Hellmers S, Krey E, Gashi A, Koschate J, Schmidt L, Stuckenschneider T, Hein A, Zieschang T. Front. Digit. Health 2023; 5: e1223845.

Copyright

(Copyright © 2023, Frontiers Media)

DOI

10.3389/fdgth.2023.1223845

PMID

37564882

PMCID

PMC10410450

Abstract

INTRODUCTION: Falls 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.

METHODS: In 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.

RESULTS: The best results showed a DeepConvLSTM with an F1 score of 0.954 (precision 0.969, recall 0.942) at the sensor position "left wrist." DISCUSSION: Since these results were obtained in the laboratory, the next step is to evaluate the suitability of the classifiers in the field.

Language: en

Keywords

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