Using WBSN for mental Healthcare monitoring

14^th World Congress on Healthcare & Technologies

July 22-23, 2019 | London, UK

Nasim Khozouie

University of Yasouj, Iran

Posters & Accepted Abstracts: J Health Med Informat

Abstract :

Todayâ??s several researches using multimodal sensing devices and communication technology and smartphones to detect human activity, health condition and mental states. Sensing types in these researches can be wearable, external and software/social media. Alternatively, we can classify sensor data in three sensing type: physiological, mental and environmental. The biomedical platforms developed in this regard can be classified into three categories: (1) monolithic platform-based, (2) textile (fabric)-based, and (3) body-sensor-network-based. WBSN is common approach for health monitoring systems. In this approach the data sense and collect by sensors and transmitted wirelessly to a base station (such as smart phone, Actigraphy devices or Smart watches) for long-term storage and processing. Typical sensors that can be found in smart phone are accelerometers, gyroscopes, ambient light sensors, proximity sensors, GPS, Bluetooth, microphone, video camera, magnetometer, etc. With the ever growing popularity and capabilities of smartphones, several research works started to use them as a platform for data collection studies. Although the sensors data is not sensing mental state itself, but can be driven of sensing behavior that is emerging from physiological data. For example, circadian rhythm disturbances have been shown in studies of activation in bipolar disorder, Skin conductivity and heart rate are factors which used to extend nervous system. In order to early detect migraine attacks are used sleep time data from wearable sensors, and so on.

Conclusion & Significance: in this present we discuss about WBSN technologies and usage machine learning model training to extract knowledge from raw data.