Abstract
Obesity has become a widespread health problem worldwide. The body mass index (BMI) is a simple and reliable index based on weight and height that is commonly used to identify and classify adults as underweight, normal, overweight (pre-obesity), or obese. In this paper, we propose a hybrid deep neural network for predicting the BMI of smartphone users, based only on the characteristics of body movement captured by the smartphone’s built-in motion sensors without any other sensitive data. The proposed deep learning model consists of four major modules: a transformation module for data preprocessing, a convolution module for extracting spatial features, a long short-term memory (LSTM) module for exploring temporal dependency, and a fully connected module for regression. We define motion entropy (MEn), which is a measure of the regularity and complexity of the motion sensor, and propose a novel MEn-based filtering strategy to select parts of sensor data that met certain thresholds for training the model. We evaluate this model using two public datasets in comparison with baseline conventional feature-based methods using leave-one-subject-out (LOSO) cross-validation. Experimental results show that the proposed model with the MEn-based filtering strategy outperforms the baseline approaches significantly. The results also show that jogging may be a more suitable activity of daily living (ADL) for BMI prediction than walking and walking upstairs. We believe that the conclusions of this study will help to develop a long-term remote health monitoring system.
Highlights
Obesity has become a widespread health problem worldwide, which may be related to an increased risk of diseases such as cardiovascular disease, diabetes, and stroke
motion entropy (MEn)-based filtering strategy, and we discuss what types of activity of daily living (ADL) might be suitable for the prediction of body mass index (BMI)
We proposed a hybrid deep neural network with a novel MEn-based filtering strategy for predicting the BMI of smartphone users using built-in motion sensors only
Summary
Obesity has become a widespread health problem worldwide, which may be related to an increased risk of diseases such as cardiovascular disease, diabetes, and stroke. Many users can use online applications on smart mobile terminals to obtain BMI These applications require personal body data including height, weight, age, and sex, which are usually very sensitive topics. (1) To the best of our knowledge, we are the first to design a hybrid deep neural network with a CNN-LSTM architecture to learn spatial features and temporal features from sensor data for identifying salient patterns related to the BMI. (3) We evaluate the hybrid deep neural network model with the MEn-based filtering strategy using two public datasets in comparison with baseline conventional feature-based approaches that have been applied to infer simple human traits from gaits [10,11].
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