Physical Fatigue Prediction Based on Heart Rate Variability (HRV) Features in Time and Frequency Domains Using Artificial Neural Networks Model During Exercise

Abstract

Awareness on fatigue level is important for people in order to understand their physiology in daily activities. This situation become more critical when involving physical exercise and reach the maximum threshold fatigue which can lead to injury. Additionally, sedentary people become the most group who is difficult to understand and know their fatigue condition based on feeling compared to the recreational exercise people and sports athlete. Therefore, this study is aims to help sedentary to predict the level of fatigue based on HRV features using artificial neural network (ANN). Eighteen sedentary peoples who are volunteer to participated in this study required to perform fatigue-induced protocol to achieve the heart rate maximum (HRmax). Those participants were run on the treadmill with speed intensities from 4km/h to 12km/h depends on their ability. During running, single-lead ECG was attached on the chest by using Ag/AgCl wet electrodes. The raw signals which accumulate together with noise and motion artefacts were then filtered in 4th order Butterworth filter. A new signal of HRV was used to analyze by extracting the features in each level of fatigue based on Edward's Method zones. Eight features of time and frequency domains were selected in the neural network as input and predicts the fatigue zones as an output. HRV and HRmax were found as significant parameters to detect fatigue by differentiate its pattern in pre and post exercise. The results reveal that the prediction model with accuracy as high as 80.6% in the output of five fatigue classes. The results presented here may facilitate improvements in identifying the level of fatigue based on prediction algorithm compared to the RPE method during physical exercise.

Heart rate variability based physical exertion monitoring for manual material handling tasks

“…However, a major limitation of their study was that instead of using RPE as a reference measure for accuracy calculations, they used the ratio of instantaneous heart rate to maximum heart rate. Although, this ratio correlates well with the RPE scores (Manzi et al 2010), it cannot entirely substitute RPE, which is considered as a gold standard for physical exertion monitoring (Ahmad et al 2020).…”

“…Their study concluded that time-domain features had higher capability to classify these phases. Interestingly, Ahmad et al (2020) used multiple HRV features to predict exertion during running exercise. These authors achieved a maximum accuracy of 81%.…”

Огляд Сучасних Підходів До Визначення Фізичної Втоми За Допомогою Моделей Штучного Інтелекту На Основі Аналізу Серцевих Сигналів

“…Цікавим є також мультипараметричний підхід до дослідження втоми. Деякі дослідники комбінують серцеві сигнали з іншими біометричними показниками, наприклад, з даними електроенцефалограми (EEG) чи електроміограми (EMG), що може покращити точність виявлення втоми [6][7][8].…”

“…Автори в роботі [8] проводили дослідження рівня втоми осіб, які ведуть сидячий спосіб життя, на основі особливостей HRV за допомогою штучних нейронних мереж. Дослідження включало 30 молодих чоловіків, які добровільно взяли участь в дослідженні.…”

Manejo Manual De Cargas: Una Aproximación De La Evidencia Al Terreno

“…Un método habitual es medir la intensidad cardiovascular mediante monitores de frecuencia cardíaca, esta actividad durante el ejercicio físico es una interesante herramienta no invasiva para monitorizar la respuesta cardiovascular al ejercicio, sin embargo, este método no proporciona una estimación de la carga en partes específicas del cuerpo (Hernando et al, 2018). Además, el uso de la RPE es un método que no debe ser sustituido por completo como referencia y siempre debe estar presente en este tipo de evaluaciones, ya que se considera un estándar de oro para la monitorización del esfuerzo físico (Ahmad et al, 2020).…”