Deep Multi-Branch Two-Stage Regression Network for Accurate Energy Expenditure Estimation With ECG and IMU Data

Abstract

Objective: Energy Expenditure (EE) estimation plays an important role in objectively evaluating physical activity and its impact on human health. EE during activity can be affected by many factors, including activity intensity, individual physical and physiological characteristics, environment, etc. However, current studies only use very limited information, such as heart rate and step count, to estimate EE, which leads to a low estimation accuracy. Methods: In this study, we proposed a deep multibranch two-stage regression network (DMTRN) to effectively fuse a variety of related information including motion information, physiological characteristics, and human physical information, which significantly improved the EE estimation accuracy. The proposed DMTRN consists of two main modules: a multi-branch convolutional neural network module which is used to extract multi-scale context features from electrocardiogram (ECG) and inertial measurement unit (IMU) data, and a two-stage regression module which aggregated the extracted multi-scale context features containing the physiological and motion information and the anthropometric features to accurately estimate EE. Results: Experiments performed on 33 participants show that our proposed method is more accurate and the average root mean square error (RMSE) is reduced by 22.8% compared with previous works. Conclusion: The EE estimation accuracy was improved by the proposed DMTRN model with a well-designed network structure and new input signal ECG. Significance: This study verified that ECG was much more effective than HR for EE estimation and cast light on EE estimation using the deep learning method.

A contactless monitoring system for accurately predicting energy expenditure during treadmill walking based on an ensemble neural network

“…The complex and diverse factors that affected EE make real-time and accurate estimation challenging, particularly for prediction tasks involving different populations. 22 , 23 In this study, we observed that the single ML algorithm did not achieve satisfactory overall prediction results, including NN algorithms with nonlinear approximation capabilities (R 2 = 0.90; Figure 1 C). A potential explanation is that the strong correlation relationship between the factors and EE in a single population disappears or is neutralized in most samples.…”

“…Developing prediction models with broad applicability and high accuracy for walking EE is challenging because of the complex time-varying and nonlinear nature of the human body, the complex and diverse factors affecting EE, and the different prediction factors and function relationships among different populations. 22 , 23 …”

“…Unlike the manual design and selection of features in ML, deep learning algorithms can automatically extract deep features without professional knowledge. 15 , 22 Although convolutional neural network (CNN) and long short-term memory (LSTM) algorithms have been used to increase the prediction accuracy of walking EE in previous studies, 15 , 22 , 34 they rely on complex time-series signals such as electrocardiography (ECG) and electromyography (EMG) and cannot achieve convenient and large-scale monitoring of walking EE.…”

Energy Expenditure Estimation in Children, Adolescents and Adults by Using a Respiratory Magnetometer Plethysmography System and a Deep Learning Model

“…Their results showed lower RMSE values with the CNN model (1.12 kcal/min) compared to the ANN model (1.73 kcal/min). Ni et al [ 55 ] estimated EE in adults from the electrocardiogram (ECG) and inertial measurement unit (IMU) using a deep multibranch two-stage regression network (DMTRN) model during the Bruce treadmill test, carried out until exhaustion. Their results showed a close correlation between the estimated EE and the reference EE measured by an indirect calorimeter (R 2 = 0.97, RMSE = 0.71 kcal/min).…”

Wearable Sensor Data Classification for Identifying Missing Transmission Sequence Using Tree Learning

“…Ni et al [ 18 ] developed a deep multi-branch two-stage regression network (DMTRN) energy expenditure (EE) estimation. The proposed approach is mainly used to predict the accurate EE which produces necessary information for healthcare centers.…”