Affiliation:
1. School of Computer Engineering and Science, Shanghai University, China
2. College of Future Industry, Gachon University, Seongnam, South Korea
3. School of Computer Science, Zhejiang University, Hangzhou, China
Abstract
With the development of the
Internet of Medical Things (IoMT)
, indoor
wireless sensor networks (WSNs)
have been used to monitor Alzheimer's disease patients daily and guide their behaviors. Alzheimer's disease may seriously impact patients’ memory, and thoughts of “what should I do” can unexpectedly form in their mind. This cognitive impairment can affect patients’ independence and well-being. As a basic infrastructure for future healthcare systems, WSN can collect patient behaviors, such as their positions and states, to support safety and health analyses. Therefore, this paper proposes a probabilistic model checking-based method to predict patient behaviors and detect abnormal behaviors related to mild cognitive impairment to help patients rebuild their confidence and perception. First, the layout of the home environment is abstracted as a formal grid, and a
user activity model (UAM)
is proposed in the form of
discrete-time Markov chain (DTMC)
to describe patients’ activity based on data collected by sensors. Second, because
Alzheimer's patients with mild cognitive impairment (A-MCI)
often forget their next daily activities, we classify and describe their daily behaviors as verification requirements in the form of
probabilistic computational tree logic (PCTL)
. Then, the UAM is input into a probabilistic model checking tool and compared against the verification property PCTL to calculate the probability values and assess temporal behaviors. As result, the activity with the largest probability is selected for behavior guidance. Third, we demonstrate the process of detecting abnormalities, including activities with abnormal temporal behaviors and activities with normal temporal behaviors but unexpected probabilities that may be repeated more than twice. The key states are extracted from the UAM to specify the verification properties for abnormality detection. Finally, a case study is presented to demonstrate the usability and feasibility of our proposed method.
Funder
National Key R&D Program of China
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications
Cited by
15 articles.
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