@article{ref1,
title="A pre-impact fall detection data segmentation method based on multi-channel convolutional neural network and class activation mapping",
journal="Physiological measurement",
year="2022",
author="Feng, Mingxu and Liu, Jizhong",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="OBJECTIVE: A segmentation method for pre-impact fall detection data is investigated. Specifically, it studies how to partition data segments that are important for classification from continuous inertial sensor data for pre-impact fall detection. APPROACH: In this study, a trigger-based algorithm combining multi-channel convolutional neural network and class activation mapping was proposed to solve the problem of data segmentation. First, a pre-impact fall detection training dataset was established and divided into two parts. For falls, the 1-second data was divided from the peak value of the acceleration signal magnitude vector to the starting direction. For activities of daily living, the cycle segmentation was performed for a 1-second window size. Second, a heat map of the class activation regions of the sensor data was formed using a multi-channel convolutional neural network and a class activation mapping algorithm. Finally, the data segmentation strategy was established based on the heat map, the basic law of falls and the real-time requirements. MAIN RESULTS: This method was verified by the SisFall dataset. The obtained segmentation strategy (i.e., to start segmenting a small data segment with a window duration of 325 ms when the acceleration signal magnitude vector is less than 9.217 m/s2) met the real-time requirements for pre-impact fall detection. Moreover, it was suitable for various machine learning algorithms, and the accuracy of the machine learning algorithms used exceeded 94.8%, with the machine learning algorithms verifying the data segmentation strategy. SIGNIFICANCE: The proposed method can automatically identify the class activation area, save the computing resources of wearable devices, shorten the duration of segmentation window, and ensure the real-time performance of pre-impact fall detection.<p /> <p>Language: en</p>",
language="en",
issn="0967-3334",
doi="10.1088/1361-6579/ac77d4",
url="http://dx.doi.org/10.1088/1361-6579/ac77d4"
}