Deep Learning-Based Optimal Smart Shoes Sensor Selection for Energy Expenditure and Heart Rate Estimation

Abstract

Wearable technologies are known to improve our quality of life. Among the various wearable devices, shoes are non-intrusive, lightweight, and can be used for outdoor activities. In this study, we estimated the energy consumption and heart rate in an environment (i.e., running on a treadmill) using smart shoes equipped with triaxial acceleration, triaxial gyroscope, and four-point pressure sensors. The proposed model uses the latest deep learning architecture which does not require any separate preprocessing. Moreover, it is possible to select the optimal sensor using a channel-wise attention mechanism to weigh the sensors depending on their contributions to the estimation of energy expenditure (EE) and heart rate (HR). The performance of the proposed model was evaluated using the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Moreover, the RMSE was 1.05 ± 0.15, MAE 0.83 ± 0.12 and R2 0.922 ± 0.005 in EE estimation. On the other hand, and RMSE was 7.87 ± 1.12, MAE 6.21 ± 0.86, and R2 0.897 ± 0.017 in HR estimation. In both estimations, the most effective sensor was the z axis of the accelerometer and gyroscope sensors. Through these results, it is demonstrated that the proposed model could contribute to the improvement of the performance of both EE and HR estimations by effectively selecting the optimal sensors during the active movements of participants.

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

“…All data sources (e.g., EE, HR, and walking speed) were aggregated to the minute level using the sliding window methods because previous studies demonstrated that the walking EE fluctuated slightly in one minute, and the longer the sliding window used, the smaller the EE estimation error. 22 , 34 If data points were missing, the single minute was excluded from the analysis, and a total of 812 single-minute samples were obtained. Data preprocessing is a necessary step before commencing the development of machine learning algorithm models.…”

“…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.…”

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

“…In order to verify the advantage of our proposed EE estimation method, we compared our method with other machine learning or deep learning algorithms including linear regression (LR) [15] [43], boosted decision tree regression (BDTR) [17], extreme gradient boosting (XGBoost) [21], random forest (RF) [18], convolutional neural network (CNN) [19] and densely connected convolutional network (DenseNet) [20] on our dataset. For machine learning algorithms, anthropometric features, motion features designed by [18], and HRV features designed by [15] were used to train the model with default parameters.…”

“…However, as most of the existing EE estimation methods were based on machine learning algorithms which need to manually design and select features, their EE estimation accuracy was still unsatisfactory. As the handcrafted features used for machine learning algorithms are highly dependent on the professional knowledge of the researchers and cannot fully reflect the effective information contained in the raw signal, deep learning algorithms which can automatically extract deep features without any professional knowledge were then proposed for EE estimation [19] [20] [27].…”

Particular Dimensions of the Social Impact of Leisure Running: Study of Poland

“…In addition to attempts to identify factors that increase the risk of injuries, newer and newer technological solutions are being developed. For example, special shoes (smart shoes) are being designed, thanks to which it is possible to achieve optimal energy expenditure and optimal heart function during physical activity and monitor daily physical activity [55]. The hardness and weight of the shoes are analyzed in terms of injuries risk [56,57].…”