An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection

Abstract

The fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors. However, these techniques do not segment by fall stages (pre-impact, impact, and post-impact) and thus useful information is lost, which may reduce the detection rate of the classifier. Aligning the segment with the fall stage is difficult, as the segment size varies. We propose an event-triggered machine learning (EvenT-ML) approach that aligns each fall stage so that the characteristic features of the fall stages are more easily recognized. To evaluate our approach, two publicly accessible datasets were used. Classification and regression tree (CART), k-nearest neighbor (k-NN), logistic regression (LR), and the support vector machine (SVM) were used to train the classifiers. EvenT-ML gives classifier F-scores of 98% for a chest-worn sensor and 92% for a waist-worn sensor, and significantly reduces the computational cost compared with the FNSW- and FOSW-based approaches, with reductions of up to 8-fold and 78-fold, respectively. EvenT-ML achieves a significantly better F-score than existing fall detection approaches. These results indicate that aligning feature segments with fall stages significantly increases the detection rate and reduces the computational cost.

A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets

“…In any case, the obtained results (with both specificity and sensitivity near 99%) were better than those published by other studies on FDS that employed the same SisFall dataset as a benchmarking tool [36,52,56,68,69] and in which a specificity and a sensitivity superior to 0.98 were not attained simultaneously.…”

A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems

“…Nevertheless, the proposal yields better results (with values for the specificity and the sensitivity around 99%) than those achieved by other works in the literature that use the same SisFall repository as the benchmarking tool [16,103,[109][110][111]. Similarly, our system also outperforms the FDS based on a Recurrent Neural Networks (RNNs) analyzed in [112], which is tested with three different datasets.…”

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