Fusion of Machine Learning and Threshold-Based Approaches for Fall Detection in Healthcare Using Inertial Sensors

Ya Wang, Peiman Sarvari, Djamel Khadraoui

2024

Abstract

In the healthcare sector, specifically for elderly care, accurate and efficient fall detection is crucial. We present an advanced fall detection methodology tailored for wearable systems. Our approach blends threshold-based screening with machine learning models like Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, and XGBoost. Utilizing 65 features extracted from the gyroscope and accelerometer data from Inertial Measurement Units, our method addresses the class imbalance often found between Activities of Daily Living and actual fall events. Threshold-based pre-screening serves to mitigate the class imbalance of the fall dataset, making the subsequent machine-learning classification more effective. Validation on two open-source IMU datasets, Sisfall and FallAllD, achieving high accuracy rates of 99.55%, 99.68% (wrist), 99.76% (waist), and 99.52% (neck), shows our model surpassing existing solutions in detection accuracy. Furthermore, our strategic feature extraction not only enhances the model’s performance but also allows for a fourfold reduction by using the 15 most important features in data transmission without sacrificing accuracy. These findings underscore the efficiency and potential of our methodology, indicating that wearables can indeed be powerful tools for high-precision fall detection with minimal data overhead.

Paper Citation

in Harvard Style

Wang Y., Sarvari P. and Khadraoui D. (2024). Fusion of Machine Learning and Threshold-Based Approaches for Fall Detection in Healthcare Using Inertial Sensors. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS; ISBN 978-989-758-688-0, SciTePress, pages 573-582. DOI: 10.5220/0012250500003657

in Bibtex Style

@conference{biosignals24,
author={Ya Wang and Peiman Sarvari and Djamel Khadraoui},
title={Fusion of Machine Learning and Threshold-Based Approaches for Fall Detection in Healthcare Using Inertial Sensors},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS},
year={2024},
pages={573-582},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012250500003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS
TI - Fusion of Machine Learning and Threshold-Based Approaches for Fall Detection in Healthcare Using Inertial Sensors
SN - 978-989-758-688-0
AU - Wang Y.
AU - Sarvari P.
AU - Khadraoui D.
PY - 2024
SP - 573
EP - 582
DO - 10.5220/0012250500003657
PB - SciTePress