Biochemical analyses of bronchoalveolar lavage fluids of healthy human volunteer smokers and nonsmokers.

Abstract

Body Sensor Networks (BSNs) are conveying notable attention due to their capabilities in supporting humans in their daily life. In particular, real-time and noninvasive monitoring of assisted livings is having great potential in many application domains, such as health care, sport/fitness, e-entertainment, social interaction and e-factory. And the basic as well as crucial feature characterizing such systems is the ability of detecting human actions and behaviors. In this paper, a novel approach for human posture recognition is proposed. Our BSN system relies on an information fusion method based on the D-S Evidence Theory, which is applied on the accelerometer data coming from multiple wearable sensors. Experimental results demonstrate that the developed prototype system is able to achieve a recognition accuracy between 98.5% and 100% for basic postures (standing, sitting, lying, squatting).