The electrocardiogram (ECG) is an emerging biometric modality that has seen about thirteen years of development, in peer-reviewed literature, and as such deserves a systematic review and discussion of the associated methods and findings. In this paper, we review most of the techniques that have been applied to the use of the electrocardiogram for biometric recognition. In particular, we categorize the methodologies based on the features and the classification schemes. Finally, a comparative analysis of the authentication performance of a few of the ECG biometric systems is presented, using our in-house database. The comparative study includes the cases where training and testing data come from the same and different sessions (days). The authentication results show that most of the algorithms that have been proposed for ECG based biometrics perform well, when the training and testing data come from the same session. However, when training and testing data come from different sessions, a performance degradation occurs. Multiple training sessions were incorporated to diminish the loss in performance. That notwithstanding, only a few of the proposed ECG recognition algorithms appear to be able to support performance improvement due to multiple training sessions. Only three of these algorithms produced This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. 2 equal error rates (EERs) in the single digits, including an EER of 5.5% using a method proposed by us.

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The challenge of heart rate monitoring based on wrist photoplethysmography (PPG) during heavy exercise is addressed. PPG is susceptible to motion artefacts, which have to be mitigated for accurate heart rate estimation. Motion artefacts are particularly apparent for wrist devices, for example, a smart watch, because of the high mobility of the arms. Proposed is a low complexity highly accurate heart rate estimation method for continuous heart rate monitoring using wrist PPG. The proposed method achieved 2.57% mean absolute error in a test data set where subjects ran for a maximum speed of 17 km/h.