Using Smartphone Sensors for Improving Energy Expenditure Estimation

Abstract

Energy expenditure (EE) estimation is an important factor in tracking personal activity and preventing chronic diseases, such as obesity and diabetes. Accurate and real-time EE estimation utilizing small wearable sensors is a difficult task, primarily because the most existing schemes work offline or use heuristics. In this paper, we focus on accurate EE estimation for tracking ambulatory activities (walking, standing, climbing upstairs, or downstairs) of a typical smartphone user. We used built-in smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately estimate EE. Using a barometer sensor, in addition to an accelerometer sensor, greatly increases the accuracy of EE estimation. Using bagged regression trees, a machine learning technique, we developed a generic regression model for EE estimation that yields upto 96% correlation with actual EE. We compare our results against the state-of-the-art calorimetry equations and consumer electronics devices (Fitbit and Nike+ FuelBand). The newly developed EE estimation algorithm demonstrated superior accuracy compared with currently available methods. The results were calibrated against COSMED K4b2 calorimeter readings.

Automatic energy expenditure measurement for health science

“…Several mobile applications and projects are being developed to track the food intake of people. The artifacts of these projects such as devices and mobile applications can help people to measure energy expenditure accurately and people might modify their personal habits so that they can improve their personal health [25].…”

A Versatile Ultrasound Simulation System for Education and Training in High-Fidelity Emergency Scenarios

“…The adopted solution exploits low-cost Radio Frequency Identification (RFID) sensors and Inertial Measurement Units (IMU): such technologies, in the last decade, has undergone a noteworthy diffusion in the context of various medical applications [22], [23]. The position of the probe is tracked using a RFID reader that acquires the code of a set of RFID markers located under the manikin skin, in the points of interest chosen by the user (e.g.…”

“…Regarding the internal electronic components, they have to perform the acquisition, the elaboration and the transmission of the data concerning the information about the probe pose, understood as the combination of its position and orientation relative to the manikin. The adopted solution exploits low-cost Radio Frequency Identification (RFID) sensors and Inertial Measurement Units (IMU): such technologies, in the last decade, has undergone a noteworthy diffusion in the context of various medical applications [22] , [23] . The position of the probe is tracked using a RFID reader that acquires the code of a set of RFID markers located under the manikin skin, in the points of interest chosen by the user (e.g.…”

A Review on Disease Inference Techniques

“…The existing literatures [9], [5] and [8], [6] are explaining some discriminant features. Where the basic methodology used in this advanced health care is explained by support Vector Machine classifier [1].The literatures [3], [4], [2], [7] following some earlier methods of health services.…”