Quantitative Estimation of Human Height and Weight Using Motion Data From Multiple Smart Devices

This article proposes a methodological framework for quantitative estimations of height and weight using behavioral data collected from smart devices. We analyze the connections between height and weight information and behavioral data from three aspects: walking speed, stride length, and step frequency and then extract two kinds of motion features including basic kinematic features and advanced features which use statistical measurements summarizing the dynamics of walking behavior over time and relative intensity of walking speed change, energy cost of one step during walking, and walking frequency, respectively, to describe the motion behavior. After that, we qualitatively and quantitatively analyze the complementarity of different motion data sources and show that more useful information existed in multisource motion data than that of only one motion data source. Based on this, we propose a feature fusion approach named Serial+CC to dealing with the relationships between all motion features from multiple smart devices and user traits of height and weight and then a fused feature set with high discrimination and low complexity is constructed. Finally, five regression models of SVM, BP neural networks, Random Forest, LSTM, and BiLSTM are built with the fused feature set. Empirical evaluations were performed on a dataset collected from 56 subjects. The results demonstrate that motion data collected from smart devices can be used for height and weight quantitative estimation. The results also illustrate our method of using motion data collected from multiple smart devices can achieve better performance than those of only using one smart devices. The best performance is achieved with average errors of 0.95% (1.59 cm) and 4.75% (2.90 kg) for height and weight estimations, respectively, in the scenario of using multiple devices.