Better health – A comprehensive and profound research about physical strength consumption estimation methods using machine learning

Abstract

In order to better evaluate and promote human health, this paper analyzes the influence of different inertial-measurement-unit signals, different sensor locations, different activity intensities and different signal fusion schemes on the accuracy of physical strength consumption estimation during walking and running activities. Different pattern recognition methods, such as the Counts-based linear regression model, the typical non-linear model based on decision tree and artificial neural network, and the end-to-end convolutional neural network model, are analyzed and compared. Our findings are as follows: 1) For the locations of sensors during walking and running activities, the physical strength consumption prediction accuracy at the ankle location is higher than that at the hip location. Therefore, wearing an inertial-measurement-unit at the ankle can improve the accuracy of the model. 2) Regarding the types of activity signals during walking and running activities, the impact of accelerometer signals on hip and ankle prediction accuracy is not significantly different, while the gyroscope model is more sensitive to the location, with higher prediction accuracy at the ankle than at the hip. In addition, the physical strength consumption prediction accuracy of accelerometer signals is higher than that of gyroscope signals, and fusion of accelerometer and gyroscope signals can improve the accuracy of physical strength consumption prediction. 3) For different data analysis models during walking and running activities, the artificial neural network model that integrates different sensor locations and inertial-measurement-unit signals with different activity intensities has the lowest mean squared error for the measurement of physical strength consumption. The non-linear models based on decision tree and artificial neural network have better physical strength consumption prediction capabilities than the Counts-based linear regression model, especially for high-intensity activity energy consumption prediction. In addition, feature engineering models are generally better than convolutional neural network model in terms of overall performance and prediction results under the three different activity intensities. Furthermore, as the activity intensity increases, the performance of all physical strength consumption calculation models decreases. We recommend using the artificial neural network model based on multi-signal fusion to estimate physical strength consumption during walking and running activities because this model exhibits strong generalization ability in cross-validation and test results, and its stability under different activity intensities is better than that of the other three models. To the best of our knowledge, this paper is the first to delve deeply and in detail into methods for estimating physical strength consumption. Undoubtedly, our paper will have an impact on research related to topics such as intelligent wearable devices and subsequent methods for estimating physical strength consumption, which are directly related to physical health.