Abstract
BackgroundWith the invention of fitness trackers, it has been possible to continuously monitor a user’s biometric data such as heart rates, number of footsteps taken, and amount of calories burned. This paper names the time series of these three types of biometric data, the user’s “activeness”, and investigates the feasibility in modeling and predicting the long-term activeness of the user.MethodsThe dataset used in this study consisted of several months of biometric time-series data gathered by seven users independently. Four recurrent neural network (RNN) architectures–as well as a deep neural network and a simple regression model–were proposed to investigate the performance on predicting the activeness of the user under various length-related hyper-parameter settings. In addition, the learned model was tested to predict the time period when the user’s activeness falls below a certain threshold.ResultsA preliminary experimental result shows that each type of activeness data exhibited a short-term autocorrelation; and among the three types of data, the consumed calories and the number of footsteps were positively correlated, while the heart rate data showed almost no correlation with neither of them. It is probably due to this characteristic of the dataset that although the RNN models produced the best results on modeling the user’s activeness, the difference was marginal; and other baseline models, especially the linear regression model, performed quite admirably as well. Further experimental results show that it is feasible to predict a user’s future activeness with precision, for example, a trained RNN model could predict–with the precision of 84%–when the user would be less active within the next hour given the latest 15 min of his activeness data.ConclusionsThis paper defines and investigates the notion of a user’s “activeness”, and shows that forecasting the long-term activeness of the user is indeed possible. Such information can be utilized by a health-related application to proactively recommend suitable events or services to the user.
Highlights
With advances in technology and ever-busy schedules, people tend to lack physical activity, and have increased level of stress
Existing works that involve time-series modeling using recurrent neural network (RNN) are presented in Section Recurrent neural networks
Our work is similar to the above studies in that we aim to model a person’s heart rates, burned calories (EE), and number of footsteps
Summary
With advances in technology and ever-busy schedules, people tend to lack physical activity, and have increased level of stress. Many health-care-related devices and services have emerged to aid users in monitoring and improving their physical wellness (Section Devices and services for wellness improvement) With wearable devices, such as fitness trackers, it has been possible to continuously observe the biometric data produced by a user, and notify the user when he/she has been physically inactive for a period of time. With the invention of fitness trackers, it has been possible to continuously monitor a user’s biometric data such as heart rates, number of footsteps taken, and amount of calories burned. A series of all-around activity trackers have been independently manufactured by Fitbit and Jawbone These fitness trackers measure the number of steps taken and log the heart rates of the wearer. This study utilized Jawbone’s “UP3” model and Fitbit’s “Charge HR” model to continuously record users’ heart rate, footsteps, and calories
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