A pre-impact fall detection data segmentation method based on multi-channel convolutional neural network and class activation mapping

Abstract

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 (CNN) 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 s 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 s window size. Second, a heat map of the class activation regions of the sensor data was formed using a multi-channel CNN 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 s−2) 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.