Abstract
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.
Highlights
Incorporating smartness in the home environment ensures security, comfort and healthcare, which are the primary goals of Ambient Assisted Living (AAL) [1]
A web of objects-based ambient assisted living framework is presented in this paper
The psychophysiological and psychometric observations of inhabitants are collected through tiny biosensors and psychiatric screening scales and objectified and virtualized to create intelligent service for ambient assisted living
Summary
Incorporating smartness in the home environment ensures security, comfort and healthcare, which are the primary goals of Ambient Assisted Living (AAL) [1]. This paper proposes an emergency psychiatric state prediction method for the AAL environment. Markov Model (HMM) [12]; objectified sensor observations and patients’ history are considered to be observations for the HMM Based on those observations, Viterbi [12], a machine learning algorithm, is used to generate the most probable psychiatric state sequence. Ambient assisted living framework: A web of objects-based smart home framework is presented in Section 3 for in-home personalized psychiatric care. The framework enables a platform to cooperate, harmonize and share the mental healthcare objects for ambient assisted living services (e.g., emergency psychiatry). The remaining sections of this paper are organized as follows: Section 2 reviews the related works; Section 3 discusses the details of the web of objects-influenced AAL framework and the emergency psychiatric state prediction model.
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