Introduction to algorithms
Machine Learning
Discriminating stress from cognitive load using a wearable EDA device
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
A Fast Multiple Longest Common Subsequence (MLCS) Algorithm
IEEE Transactions on Knowledge and Data Engineering
The MONARCA self-assessment system: a persuasive personal monitoring system for bipolar patients
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
FEEL: frequent EDA and event logging -- a mobile social interaction stress monitoring system
CHI '12 Extended Abstracts on Human Factors in Computing Systems
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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.