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

Sandra Hellmers; Elias Krey; Arber Gashi; Jessica Koschate; Laura Schmidt; Tim Stuckenschneider; Andreas Hein; Tania Zieschang

doi:10.3389/fdgth.2023.1223845

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

Front Digit Health. 2023 Jul 26:5:1223845. doi: 10.3389/fdgth.2023.1223845. eCollection 2023.

Authors

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

Affiliations

¹ Assistance Systems and Medical Device Technology, Department for Health Services Research, Carl von Ossietzky University, Oldenburg, Germany.
² Geriatric Medicine, Department for Health Services Research, Carl von Ossietzky University, Oldenburg, Germany.

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.

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

Grants and funding

The project has been funded by the Federal Ministry of Education and Research (BMBF, grant number: 01GY2021). The BMBF did not contribute to the study design or in writing the manuscript nor will it be involved in data collection, analysis and interpretation.