Automatic Subject Identification Using Scale-Based Ballistocardiogram Signals

Many electronic devices such as weighing scales, fitness equipment and medical devices are nowadays shared by multiple users. In such devices, automatic identification of device users becomes an important step towards improved user convenience and personalized service. In this paper, we propose a novel approach for subject identification using ballistocardiogram (BCG) signals collected unobtrusively from a modified weighing scale. Our approach first segments BCG signals into heartbeats using signal filtering and beat detection techniques, and averages beats to obtain smoother ensemble averaged BCG frames that are more robust to noise. Second, it extracts features related to subjects’ cardiovascular performance and musculoskeletal system from their BCG frames. Finally, it trains a machine learning model for predicting the owner of an unlabeled BCG recording based on its features. We evaluated our approach through a pilot experimental study with subjects’ BCG signals recorded at rest and following different physiological modulation. Our approach achieves up to 97% identification accuracy at rest conditions and incurs a 15–20% accuracy drop on average under physiological modulation.