(untitled) (International Journal of Engineering and Computer Science, 2015)
The rapid aging of the world’s population, along with an increase in the prevalence of chronic illnesses and obesity, requires adaption and modification of current healthcare models. One such approach involves tele-health applications, many of which are based on sensor technologies for unobtrusive monitoring. Recent technological advances, in particular, involving micro-electro-mechanical systems, have resulted in miniaturized wearable devices that can be used for a range of applications. One of the leading areas for utilization of bodyfixed sensors is the monitoring of human movement. This paper has presented the activity recognition method for children using only a triaxial accelerometer and a barometric pressure sensor. Time-domain and frequency-domain features are extracted for categorizing body postures such as standing still and wiggling as well as locomotion such as toddling and crawling. In this approach, we use a single 3-axis accelerometer and a barometric pressure sensor worn on a waist of the body to prevent child accidents such as unintentional injuries at home. Labeled accelerometer data are collected from children of both sexes up to the age of 16 to 29 months. To recognize daily activities, mean, standard deviation, and slope of time-domain features are calculated over sliding windows. In addition, the FFT analysis is adopted to extract frequency-domain features of the aggregated data, and then energy and correlation of acceleration data are calculated. Child activities are classified into 11 daily activities which are wiggling, rolling, standing still, standing up, sitting down, walking, toddling, crawling, climbing up, climbing down, and stopping.