Identification of Persons Based on Electrocardiogram and Motion Data

Wearable sensors enable to monitor patients in their everyday living and working environments. However, since they generate highly personal data, their protection from misuse or unauthorized use is crucial. Under certain conditions, it is important to examine whether the data they generate uniquely describe their deployment environment. This can be used as an additional measure to certify the authenticity of the data. In this paper, we uniquely identify persons based on the measurements of wireless electrocardiograms and motion sensors. We let 34 subjects perform 7 different activities while synchronously sampling a 5-lead wireless electrocardiogram, two 3D accelerometers, and a 3D gyroscope. Using both time-and frequency-domain features, we train a multi-layer CNN to achieve a classification accuracy exceeding 98%. Our model is able to uniquely classify 88% of the subjects with 100% accuracy, even though more than 82% of them have similar physical constitution (they are young people between the ages of 21 and 24, with a mean age of 22 years and standard deviation of 1.9 years). In order to demonstrate the variability of the data, we perform activity recognition using the same datasets; the model is able to classify the activities with an average accuracy of 92%.