AUTHOR=Hellmers Sandra , Krey Elias , Gashi Arber , Koschate Jessica , Schmidt Laura , Stuckenschneider Tim , Hein Andreas , Zieschang Tania TITLE=Comparison of machine learning approaches for near-fall-detection with motion sensors JOURNAL=Frontiers in Digital Health VOLUME=5 YEAR=2023 URL=https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2023.1223845 DOI=10.3389/fdgth.2023.1223845 ISSN=2673-253X 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.