Radar sensing technology for fall detection under near real-life conditions

Currently available technological solutions do not allow to reliably detect falls in the elderly, due to still-open issues on both sensing and processing sides. In fact, the most promising sensing approaches raise concerns for privacy issues (e.g., vision-based approaches) or low acceptability rate (e.g., wearable-based approaches); whereas on the processing side, commonly used methodologies are based on supervised techniques trained with both positive (falls) and negative (non-fall) samples, both simulated by healthy young subjects. As a result of such a training protocol, fall detectors inevitably exhibit lower performance when used in real-life conditions, in which monitored subjects are older adults. In order to address the problem of fall detection under real-life conditions, this study investigates privacy-preserving unobtrusive sensing technologies together with an unsupervised methodology trained exclusively on daily activity (non-fall) data from the monitored elderly subject. Preliminary results are very encouraging, showing the effectiveness to achieve a good detection performance and, most importantly, which is more reproducible in real-life scenarios.