Radoje Milić

Context-based ensemble method for human energy expenditure estimation

Multiple Contexts Ensemble (MCE) method to estimate the human energy expenditure (EE).MCE outperforms conventional regression approaches, ensembles and BodyMedia EE device.MCE provides better accuracy than using only the activity of the user as the context.MCE is independent of the machine learning algorithm, thus any algorithm can be used. Monitoring human energy expenditure (EE) is important in many health and sports applications, since the energy expenditure directly reflects the intensity of physical activity. The actual energy expenditure is unpractical to measure; therefore, it is often estimated from the physical activity measured with accelerometers and other sensors. Previous studies have demonstrated that using a persons activity as the context in which the EE is estimated, and using multiple sensors, improves the estimation. In this study, we go a step further by proposing a context-based reasoning method that uses multiple contexts provided by multiple sensors. The proposed Multiple Contexts Ensemble (MCE) approach first extracts multiple features from the sensor data. Each feature is used as a context for which multiple regression models are built using the remaining features as training data: for each value of the context feature, a regression model is trained on a subset of the dataset with that value. When evaluating a data sample, the models corresponding to the context (feature) values in the evaluated sample are assembled into an ensemble of regression models that estimates the EE of the user. Experiments showed that the MCE method outperforms (in terms of lower root means squared error and lower mean absolute error): (i) five single-regression approaches (linear and non-linear); (ii) two ensemble approaches: Bagging and Random subspace; (iii) an approach that uses artificial neural networks trained on accelerometer-data only; and (iv) BodyMedia (a state-of-the-art commercial EE-estimation device).

Ensembles of multiple sensors for human energy expenditure estimation

Monitoring human energy expenditure is important in many health and sport applications, since the energy expenditure directly reflects the level of physical activity. The actual energy expenditure is unpractical to measure; hence, the field aims at estimating it by measuring the physical activity with accelerometers and other sensors. Current advanced estimators use a context-dependent approach in which a different regression model is invoked for different activities of the user. In this paper, we go a step further and use multiple contexts corresponding to multiple sensors, resulting in an ensemble of models for energy expenditure estimation. This provides a multi-view perspective, which leads to a better estimation of the energy. The proposed method was experimentally evaluated on a comprehensive set of activities where it outperformed the current state-of-the-art.

Estimating Energy Expenditure With Multiple Models Using Different Wearable Sensors

This paper presents an approach to designing a method for the estimation of human energy expenditure (EE). The approach first evaluates different sensors and their combinations. After that, multiple regression models are trained utilizing data from different sensors. The EE estimation method designed in this way was evaluated on a dataset containing a wide range of activities. It was compared against three competing state-of-the-art approaches, including the BodyMedia Fit armband, the leading consumer EE estimation device. The results show that the proposed method outperforms the competition by up to 10.2 percentage points.

Towards Human Energy Expenditure Estimation Using Smart Phone Inertial Sensors

This paper is focused on a machine-learning approach for estimating human energy expenditure during sport and normal daily activities. The paper presents technical feasibility assessment that analyses requirements and applicability of smart phone sensors to human energy expenditure. The paper compares and evaluates three different sensor configuration sets: (i) a heart rate monitor and two standard inertial sensors attached to the users thigh and chest; (ii) a heart rate monitor with an embedded inertial sensor and a smart phone carried in the pocket; and (iii) only a smart phone carried in the pocket. The accuracy of the models is validated against indirect calorimetry using the Cosmed system and compared to a commercial device for energy expenditure SenseWear armband. The results show that models trained using relevant features can perform comparable or even better than available commercial device.

Does Physical Fatigue Affect Color Vision

The purpose of this study was to establish whether physical fatigue affects color vision. Thirty healthy participants were included in the study (M:F=15:15), age 25.3±4.4 y, all professional or top amateur athletes. They were exhausted using the Wingate test (WT). Physical fatigue was determined by blood lactate level before the WT and 1, 3, 5, 7 and 10 min after. Color vision was evaluated using the Hardy-Rand-Rittler (HRR) and the Mollon-Reffin Minimalist (MRM) tests before the WT and 5, 10 and 30 min after. Five minutes after the WT 2/30 (6%) showed affected color vision in the protan axis and 25/30 (83%) in the tritan axis. Ten and 30 min after the WT all the participants showed normal color vision in both the deutan and protan axes, whereas 12/30 (40%) and 8/30 (26%), respectively, showed affected color vision in the tritan axis. A gender difference was observed in color vision deficiency and improvement, with female participants being affected more and longer. The study showed that intense physical effort affects color vision with the tritan axis being predominantly affected.