Deep Learning Multi-Class Approach for Human Fall Detection Based on Doppler Signatures

Abstract

Falling events are a global health concern with short- and long-term physical and psychological implications, especially for the elderly population. This work aims to monitor human activity in an indoor environment and recognize falling events without requiring users to carry a device or sensor on their bodies. A sensing platform based on the transmission of a continuous wave (CW) radio-frequency (RF) probe signal was developed using general-purpose equipment. The CW probe signal is similar to the pilot subcarriers transmitted by commercial off-the-shelf WiFi devices. As a result, our methodology can easily be integrated into a joint radio sensing and communication scheme. The sensing process is carried out by analyzing the changes in phase, amplitude, and frequency that the probe signal suffers when it is reflected or scattered by static and moving bodies. These features are commonly extracted from the channel state information (CSI) of WiFi signals. However, CSI relies on complex data acquisition and channel estimation processes. Doppler radars have also been used to monitor human activity. While effective, a radar-based fall detection system requires dedicated hardware. In this paper, we follow an alternative method to characterize falling events on the basis of the Doppler signatures imprinted on the CW probe signal by a falling person. A multi-class deep learning framework for classification was conceived to differentiate falling events from other activities that can be performed in indoor environments. Two neural network models were implemented. The first is based on a long-short-term memory network (LSTM) and the second on a convolutional neural network (CNN). A series of experiments comprising 11 subjects were conducted to collect empirical data and test the system's performance. Falls were detected with an accuracy of 92.1% for the LSTM case, while for the CNN, an accuracy rate of 92.1% was obtained. The results demonstrate the viability of human fall detection based on a radio sensing system such as the one described in this paper.

Keywords: CNN; Doppler signatures; LSTM; WiFi; elderly healthcare; fall detection.

Figure 1

Overview of the fall detection system based on Doppler signatures.

Figure 2

Test scenario for fall detection experiments and positioning of the measurement equipment.

Figure 3

Spectrograms of the four activities performed in the indoor environment.

Figure 4

Snapshot of the probe signal during a falling event without applying a pre-processing stage.

Figure 5

Probe signal pre-processed with denoising filter.

Figure 6

Spectrograms of the activities performed during the experimentation protocol after noise removal.

Figure 7

The architecture of the LSTM network implemented for the classification process, where n is the size of the input data set.

Figure 8

The architecture of the CNN network implemented for the classification process, where n is the size of the input data set.

Figure 9

LSTM multi-class classification confusion matrix results, where the classes correspond to (1) no activity, (2) walking, (3) going up/down stairs, and (4) falling.

Figure 10

CNN multi-class classification confusion matrix results, where the classes correspond to (1) no activity, (2) walking, (3) going up/down stairs, and (4) falling.

Figure 11

Confusion matrices of both classification frameworks where the classes correspond to (1) no activity, (2) walking, and (3) falling.

Figure 12

ROC curves computed for both classification frameworks, where class 0 corresponds to no activity, class 1 to walking, and class 2 to falling.