A machine learning approach for contactless heart rate monitoring using a webcam reduced the root mean squared error of HR detection from 43.76 to 3.64 beats/min in naturalistic measurements.
Does a machine learning approach improve the accuracy of contactless heart rate monitoring using a webcam?
A machine learning approach significantly improves the accuracy of webcam-based contactless heart rate monitoring in naturalistic settings.
Absolute Event Rate: 3.64% vs 43.76%
Unobtrusive, contactless recordings of physiological signals are very important for many health and human-computer interaction applications. Most current systems require sensors which intrusively touch the user's skin. Recent advances in contact-free physiological signals open the door to many new types of applications. This technology promises to measure heart rate (HR) and respiration using video only. The effectiveness of this technology, its limitations, and ways of overcoming them deserves particular attention. In this paper, we evaluate this technique for measuring HR in a controlled situation, in a naturalistic computer interaction session, and in an exercise situation. For comparison, HR was measured simultaneously using an electrocardiography device during all sessions. The results replicated the published results in controlled situations, but show that they cannot yet be considered as a valid measure of HR in naturalistic human-computer interaction. We propose a machine learning approach to improve the accuracy of HR detection in naturalistic measurements. The results demonstrate that the root mean squared error is reduced from 43.76 to 3.64 beats/min using the proposed method.
Monkaresi et al. (Fri,) conducted a other in Heart rate monitoring. Machine learning approach for contactless heart rate monitoring vs. Standard contactless measurement was evaluated on Root mean squared error of heart rate detection. A machine learning approach for contactless heart rate monitoring using a webcam reduced the root mean squared error of HR detection from 43.76 to 3.64 beats/min in naturalistic measurements.