  This paper reports on an in-depth study of electrocardiogram (ECG) biometrics
in everyday life. We collected ECG data from 20 people over a week, using a
non-medical chest tracker. We evaluated user identification accuracy in several
scenarios and observed equal error rates of 9.15% to 21.91%, heavily depending
on 1) the number of days used for training, and 2) the number of heartbeats
used per identification decision. We conclude that ECG biometrics can work in
the wild but are less robust than expected based on the literature,
highlighting that previous lab studies obtained highly optimistic results with
regard to real life deployments. We explain this with noise due to changing
body postures and states as well as interrupted measures. We conclude with
implications for future research and the design of ECG biometrics systems for
real world deployments, including critical reflections on privacy.
