Characterization and reduction of exercise-based motion influence on heart rate variability using accelerator signals and channel decoding in the time–frequency domain

Abstract

In our proposed method, we utilized the motion trajectory (which is known to exist partially in HRV) measured by a three-channel accelerator (ACC). We then estimated their shares in HRV using a wearable electrocardiogram (ECG) and an error-correcting problem formulation. In this method, we characterized the motion components of three orthogonal directions induced into the HRV signal, and then we suppressed the estimated motion artefact to construct a motion-attenuated spectrogram. Main results and Significance: Our analysis showed that HRV in the exercise context is susceptible to motion artefacts. Furthermore, the interpretation of autonomic nervous system (ANS) activity and HRV indices throughout exercise has a high margin of error depending on the intensity level, type of exercise, and motion trajectory. Our experiment on 84 healthy subjects throughout mid-intensity cycling and walking tests showed 39% and 32% influence on average, respectively. In addition, our proposed method revealed through a maximal running test with 11 runners that motion can describe on average 20%-40% of the HRV high-frequency (HF) energy at different workloads of running.

Assessing Drivers’ Physiological Responses Using Consumer Grade Devices

“…Since with wearable devices such as Faros, the experimental conditions differ and are not always performed in a controlled laboratory environment, the gathered data can be affected by motion artefacts. Alikhani et al found that motion can describe the high frequencies in HRV up to 40% [68]. However, the noise of motion can be eliminated by adding an additional accelerometer on each electrode [69].…”