Cloud based mental state monitoring system for suicide risk reconnaissance using wearable bio-sensors

Development of wireless body- and bio-sensor network opens a new horizon of healthcare especially to measure the vital physical signs of personnel for disease diagnosis and patient monitoring. And the cloud computing technology has enabled patient monitoring service dynamically scalable and ubiquitously accessible. The success of suicide risk reconnaissance is depends on effective prediction of mental states. This paper proposes a suicide risk scouting prototype by predicting mental states in cloud environment. In this system, patients' real-time vital diseases symptoms are collected through wireless body area network (WBAN) and then analyzed the collected data in healthcare cloud platform with patient's historical repository of diseases, habits, rehabilitations and genetics. Here, the mental statuses of patients have been modeled as the discrete set of states of hidden Markov model (HMM), where WBANs annotations and stored facts of patients in cloud are considered as the observations of HMM. Subsequently, the Viterbi, a machine learning algorithm has been applied to generate the most probable mental state sequence to monitor suicide risk of mentally disordered patients. Finally, the proposed system is validated by deploying this model on mental patients dataset.