Unsupervised Learning of Fall Incidents using Radar-based Sensing

Automatic fall detection using radar technology significantly advances assisted living and smarter healthcare solutions. In this paper, a novel unsupervised method for detecting fall incidents in human daily activities is proposed. By analyzing ultra-wideband radar returns, a radar time series is obtained and used to find the time-frequency signatures of different activities. These signatures are subsequently binarized and used as input to a deep stacked auto-encoder network for latent feature extraction. The latent features are then clustered through a clustering layer that leverages an auxiliary distribution function to fine-tune the samples clustered into fall and non-fall groups. The proposed fall clustering method is compared against several clustering approaches in terms of accuracy of clustered samples, normalized mutual information and adjusted rand index metrics. It is shown that the proposed method realizes automatic latent feature learning from the radar data and can distinguish fall from non-fall classes by uncovering patterns in a data set with no pre-existing labels. The results show that the proposed unsupervised fall detection method outperforms the other approaches in terms of providing higher accuracy of the clustered data samples. The advantage of the proposed method is that it does not need a large dataset to achieve distinctiveness between clusters.