Web of Objects Based Ambient Assisted Living Framework for Emergency Psychiatric State Prediction

作     者：Alam, Md Golam Rabiul Abedin, Sarder Fakhrul Al Ameen, Moshaddique Hong, Choong Seon 

作者机构：Kyung Hee Univ Comp Sci & Engn 1732 Deokyoungdaero Yongin 446701 South Korea 

出 版 物：《SENSORS》 (传感器)

年 卷 期：2016年第16卷第9期

页      面：1431-1431页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

基　　金：MSIP, Korea, under the G-ITRC support program [IITP-2016-R6812-15-0001] National Research Foundation of Korea [21A20131612192] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS) 

主　　题：ambient assisted living web of objects mental healthcare emergency psychiatry smart home 

摘      要：Ambient assisted living can facilitate optimum health and wellness by aiding physical, mental and social well-being. In this paper, patients psychiatric symptoms are collected through lightweight biosensors and web-based psychiatric screening scales in a smart home environment and then analyzed through machine learning algorithms to provide ambient intelligence in a psychiatric emergency. The psychiatric states are modeled through a Hidden Markov Model (HMM), and the model parameters are estimated using a Viterbi path counting and scalable Stochastic Variational Inference (SVI)-based training algorithm. The most likely psychiatric state sequence of the corresponding observation sequence is determined, and an emergency psychiatric state is predicted through the proposed algorithm. Moreover, to enable personalized psychiatric emergency care, a service a web of objects-based framework is proposed for a smart-home environment. In this framework, the biosensor observations and the psychiatric rating scales are objectified and virtualized in the web space. Then, the web of objects of sensor observations and psychiatric rating scores are used to assess the dweller s mental health status and to predict an emergency psychiatric state. The proposed psychiatric state prediction algorithm reported 83.03 percent prediction accuracy in an empirical performance study.