Improving Energy Expenditure Estimation through Activity Classification and Walking Speed Estimation Using a Smartwatch.

Energy expenditure (EE) estimation is an important factor in tracking personal activity and preventing chronic diseases, such as obesity and diabetes. The challenge is to provide accurate EE estimations in free-living environment through portable and unobtrusive devices. In this paper, we present an experimental study to estimate energy expenditure during sitting, standing and treadmill walking using a smartwatch.

Detection of Sleep and Wake States Based on the Combined Use of Actigraphy and Ballistocardiography.

This paper presents a combined use of actigraphy and ballistocardiography to measure sleep stages without a disruptive sleep environment. Although polysomnography (PSG) is considered the gold standard for measuring sleep stages, the intrusive setup may lead to an unnatural sleep and impact the actual sleep quality. To address this issue a novel approach to measure sleep stages is presented by combining the acceleration measurements of actigraphy with the cardiological measurements of ballistocardiography.

Use of Average Vertical Velocity and Difference in Altitude for Improving Automatic Fall Detection from Trunk Based Inertial and Barometric Pressure Measurements.

Despite the extensive research that has been carried out on automatic fall detection using wearable sensors, falls in the elderly cannot be detected effectively yet. Although recent fall detection algorithms that evaluate the descent, impact and post impact phases of falls, often using vertical velocity, vertical acceleration and trunk angle respectively, tend to be more accurate than the algorithms that do not consider them, they still lack the desired accuracy required to be used among frail older adults. This study aims to improve the accuracy of fall detection algorithms by incorporating average vertical velocity and difference in altitude as additional parameters to the vertical velocity, vertical acceleration and trunk angle parameters.

Combined Regression and Classification Models for Accurate Estimation of Walking Speed Using a Wrist-worn IMU.

Walking speed is an important quantity not only in fitness applications but also for Iifestyle and health monitoring purposes. With the recent advances in MEMS technology, miniature body-worn sensors have been used for ambulatory walking speed estimation using regression models. However, studies show that these models are more prone to errors in slow walking regime compared to normal and fast walking regimes.