Privacy-Preserving Fall Detection with Deep Learning on mmWave Radar Signal

Fall is one of the main reasons for body injuries among seniors. Traditional fall detection methods are mainly achieved by wearable and non-wearable techniques, which may cause skin discomfort or invasion of privacy to users. In this paper, we propose an automatic fall detection method with the assist of the mmWave radar signal to solve the aforementioned issues. The radar devices are capable to record the reflection from objects in both the spatial and temporal domain, which can be used to depict the activities of users with the support of a recurrent neural network (RNN) with long-short-term memory (LSTM) units. First, we employ the radar low-dimension embedding (RLDE) algorithm to preprocess the Range-angle reflection heatmap sequence converted from the raw radar signal for reducing the redundancy in the spatial domain. Then, the processed sequence is split into frames for inputting LSTM units one by one. Eventually, the output from the last LSTM unit is fed in a Softmax layer for classifying different activities. To validate the effectiveness of our proposed method, we construct a radar dataset with the assist of market radar module devices, to implement several experiments. The experimental results demonstrate that, compared to LSTM only and the widely used 3-D convolutional neural network (3-D CNN), combining RLDE and LSTM can achieve the best detection results with much less computational time consumption. In addition, we extend the proposed method to classify multiple human activities simultaneously and the satisfied performances are observed.