LAUSR

Indoor human speed estimation is critical to in-home health monitoring of elderly people since it can provide the moving status of the human. Contactless indoor speed estimation with radio signals is challenging due to the complicated relationship between the speed of moving human and radio signals. In this article, we propose an indoor speed estimation framework, SpeedNet, to estimate the speed from the radio signals. Specifically, SpeedNet first extracts the dominant path signal reflected from the human through the beamforming technique. Then, SpeedNet obtains the doppler frequency shift (DFS) corresponding to the moving human by analyzing the short-time Fourier transform (STFT) spectrogram of the dominant path signal. Finally, SpeedNet trains a deep neural network composed of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to utilize the spatial and temporal features of DFS to estimate the speed of moving human. The experimental results show that SpeedNet can estimate the human moving speed with an average accuracy of 96.33% in a typical indoor environment, which is better than the state-of-the-art approaches.