Lightweight neural network based model for real-time precise HR monitoring during high intensity workout using consumer smartwatches.

Abstract

Mobile and wearable electronics is one of the rapidly developing areas of high technologies, which regularly appear new devices that offer new features for monitoring our health, level of physical exertion and everyday activity. From the point of view of medicine and sports, usually wearable devices are used to solve one of two tasks: round-the-clock continuous heart rate monitoring, and the control of the heart rate during workout. One of the drawbacks of such devices is the calculation errors that are caused by the motion artifacts. The most critical of them are those that arise in some cases when performing high-intensity training's, which are conjugate with intense hands movements, such as sprint running or boxing. In addition, limited resources of wearable devices do not always allow usage of very complex algorithms for processing certain events. In our work, an ultra-lightweight framework for a precise real-time heart rate monitoring during the high intensity physical exercises is developed. The model is a combination of a digital signal processing and deep convolutional and recurrent neural networks approaches. From the personalization point of view, the effect of anthropometric parameters has been studied. The model shows an average mean absolute error of 2.4±2.8 bpm during 5 fold cross-validation on an internal dataset, 2.9±3.4 bpm when evaluated on 12 IEEE SPC subjects, and 4.8±5.3 bpm when evaluated on 24 subjects from Wonkwang University dataset. Such results exceed the current state of the art solutions both in terms of the achieved accuracy of heart rate estimation and consumed computational resources. Clinical relevance- This work aims to provide a robust and lightweight neural network based framework for a real time heart rate estimation during high intensity physical exercises.