Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers

Human activity recognition (HAR) by wearable sensor devices embedded in the Internet of things (IOT) can play a significant role in remote health monitoring and emergency notification, to provide healthcare of higher standards. The purpose of this study is to investigate a human activity recognition method of accrued decision accuracy and speed of execution to be applicable in healthcare. This method classifies wearable sensor acceleration time series data of human movement using efficient classifier combination of feature engineering-based and feature learning-based data representation. Leave-one-subject-out cross-validation of the method with data acquired from 44 subjects wearing a single waist-worn accelerometer on a smart textile, and engaged in a variety of 10 activities, yields an average recognition rate of 90%, performing significantly better than individual classifiers. The method easily accommodates functional and computational parallelization to bring execution time significantly down

Modelling and simulation of drying phenomena with rheological behaviour

Foods have high moisture contents which are lost during the drying process. This water loss can produce important changes in size that hinder the analysis of heat and mass transport. A model of simultaneous heat and moisture transfer in parallelepiped-shaped potato samples was coupled with a model of its elastic behaviour. Governing equations and boundary conditions were solved numerically using the finite difference method. To check the validity of the mathematical model, drying experiments were carried out. Experimental conditions were as follows: drying temperatures of 38, 42 and 47°C; relative humidities of 14, 20 and 23% and air velocities of 1.5, 3 and 4.5 m/s. The different air velocities and temperatures were used in the drying process to determine their effects on drying time. It was obtained the range of moisture content in the sample and the range of temperature and stress during drying time. Comparison between predicted and experimental results provides satisfactory agreement

Simultaneous heat and mass transfer in packed bed brying of seeds having a mucilage coating

The simultaneous heat and mass transfer between fluid phase and seeds having a mucilaginous coating was studied during packed bed drying. To describe the process, a two-phase model approach was employed, in which the effects of bed shrinkage and nonconstant physical properties were considered. The model took into account bed contraction by employing moving coordinates. Equations relating shrinkage and structural parameters of the packed bed with moisture content, required in the drying model, were developed from experimental results in thick-layer bed drying. The model verification was based on a comparison between experimental and predicted data on moisture content and temperature along the bed. Parametric studies showed that the application of correlations capable of incorporating changes in bed properties gives better data simulation. By experimental-theoretical analysis, the importance of shrinkage for a more accurate interpretation of heat and mass transfer phenomena in the drying of porous media composed of mucilaginous seeds is corroborated