Meaningful Data Treatment from Multiple Physiological Sensors in a Cyber-Physical System

Once specific Smart Sensors are designed and manufactured in the newest nanoelectronics technology, and a Wireless Sensor Network is designed for being used on wearable applications (Cyber-Physical System) with optimum performance (data rate, power consumption, comfortability, etc.) the next step is the treatment applicable to the large amount of data collected. This can be a very general, and sometimes an unaffordable problem, but considering a system collecting physiological data from smart sensors on a human body, the range of possibilities is restricted to health or leisure but also to safety. In this case, a finite, and well-located, number of physiological sensors are producing few data per unit of time, which are locally processed for obtaining a reduced set of characteristics that are globally analyzed. In this paper, an analysis on different approaches for combining data from smart sensors attached to human body, with the purpose of determining the main emotion present in the person, is presented. Machine learning, selection of the best characteristics from raw sensor data, databases for system training, etc. are the key aspects in this problem. The conclusions of the analysis will help in the design of a new application, where emotion detection can be used for personal safety (domestic violence, sexual violence, bullying, etc.). Attention is paid on the locally and globally data processing in terms of hardware and software, together with low-power behavior.