Source of Publication
In recent years, the declining birthrate and aging population have gradually brought countries into an ageing society. Regarding accidents that occur amongst the elderly, falls are an essential problem that quickly causes indirect physical loss. In this paper, we propose a pose estimation-based fall detection algorithm to detect fall risks. We use body ratio, acceleration and deflection as key features instead of using the body keypoints coordinates. Since fall data is rare in real-world situations, we train and evaluate our approach in a highly imbalanced data setting. We assess not only different imbalanced data handling methods but also different machine learning algorithms. After oversampling on our training data, the K-Nearest Neighbors (KNN) algorithm achieves the best performance. The F1 scores for three different classes, Normal, Fall, and Lying, are 1.00, 0.85 and 0.96, which is comparable to previous research. The experiment shows that our approach is more interpretable with the key feature from skeleton information. Moreover, it can apply in multi-people scenarios and has robustness on medium occlusion.
Institute of Electrical and Electronics Engineers (IEEE)
Anomaly Detection, Data Sampling, Fall Detection, Machine Learning, Pose Estimation
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Liu, Yen-Hung; Hung, Patrick C. K.; Iqbal, Farkhund; and Fung, Benjamin C. M., "Automatic Fall Risk Detection based on Imbalanced Data" (2021). All Works. 4728.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series