  Objective: Continuous monitoring of biosignals via wearable sensors has
quickly expanded in the medical and wellness fields. At rest, automatic
detection of vital parameters is generally accurate. However, in conditions
such as high-intensity exercise, sudden physiological changes occur to the
signals, compromising the robustness of standard algorithms. Methods: Our
method, called BayeSlope, is based on unsupervised learning, Bayesian
filtering, and non-linear normalization to enhance and correctly detect the R
peaks according to their expected positions in the ECG. Furthermore, as
BayeSlope is computationally heavy and can drain the device battery quickly, we
propose an online design that adapts its robustness to sudden physiological
changes, and its complexity to the heterogeneous resources of modern embedded
platforms. This method combines BayeSlope with a lightweight algorithm,
executed in cores with different capabilities, to reduce the energy consumption
while preserving the accuracy. Results: BayeSlope achieves an F1 score of 99.3%
in experiments during intense cycling exercise with 20 subjects. Additionally,
the online adaptive process achieves an F1 score of 99% across five different
exercise intensities, with a total energy consumption of 1.55+-0.54~mJ.
Conclusion: We propose a highly accurate and robust method, and a complete
energy-efficient implementation in a modern ultra-low-power embedded platform
to improve R peak detection in challenging conditions, such as during
high-intensity exercise. Significance: The experiments show that BayeSlope
outperforms a state-of-the-art algorithm up to 8.4% in F1 score, while our
online adaptive method can reach energy savings up to 38.7% on modern
heterogeneous wearable platforms.

Signal Processing;Machine Learning;Systems and Control;Systems and Control