The eMouveRecherche application competes with research devices to evaluate energy expenditure, physical activity and still time in free-living conditions.

The proliferation of smartphones is creating new opportunities to monitor and interact with human subjects in free-living conditions since smartphones are familiar to large segments of the population and facilitate data collection, transmission and analysis. From accelerometry data collected by smartphones, the present work aims to estimate time spent in different activity categories and the energy expenditure in free-living conditions. Our research encompasses the definition of an energy-saving function (PredEE) considering four physical categories of activities (still, light, moderate and vigorous), their duration and metabolic cost (MET). To create an efficient discrimination function, the method consists of classifying accelerometry-transformed signals into categories and of associating each category with corresponding Metabolic Equivalent Tasks. The performance of the PredEE function was compared with two previously published functions (f(η,d)aedes,f(η,d)nrjsi), and with two dedicated sensors (Armband® and Actiheart®) in free-living conditions over a 12-h monitoring period using 30 volunteers. Compared to the two previous functions, PredEE was the only one able to provide estimations of time spent in each activity category. In relative value, all the activity categories were evaluated similarly to those given by Armband®. Compared to Actiheart®, the function underestimated still activities by 10.1% and overestimated light- and moderate-intensity activities by 7.9% and 4.2%, respectively. The total energy expenditure error produced by PredEE compared to Armband® was lower than those given by the two previous functions (5.7% vs. 14.1% and 17.0%). PredEE provides the user with an accurate physical activity feedback which should help self-monitoring in free-living conditions.

A smartphone-driven methodology for estimating physical activities and energy expenditure in free living conditions.

This paper introduces a function dedicated to the estimation of total energy expenditure (TEE) of daily activities based on data from accelerometers integrated into smartphones. The use of mass-market sensors such as accelerometers offers a promising solution for the general public due to the growing smartphone market over the last decade. The TEE estimation function quality was evaluated using data from intensive numerical experiments based, first, on 12 volunteers equipped with a smartphone and two research sensors (Armband and Actiheart) in controlled conditions (CC) and, then, on 30 other volunteers in free-living conditions (FLC). The TEE given by these two sensors in both conditions and estimated from the metabolic equivalent tasks (MET) in CC served as references during the creation and evaluation of the function. The TEE mean gap in absolute value between the function and the three references was 7.0%, 16.4% and 2.7% in CC, and 17.0% and 23.7% according to Armband and Actiheart, respectively, in FLC. This is the first step in the definition of a new feedback mechanism that promotes self-management and daily-efficiency evaluation of physical activity as part of an information system dedicated to the prevention of chronic diseases.