Selective sensing of a heterogeneous population of units with dynamic health conditions. (2nd December 2018)

• Record Type: Journal Article
• Authors: Lin, Ying; Liu, Shan; Huang, Shuai
• Is Part Of: IISE transactions. Volume 50:Number 12(2018)
• Page Start: 1076
• Page End: 1088
• Publication Date: 2018-12-02
• Subjects: Dynamic system monitoring -- heterogeneous population -- selective sensing; Industrial engineering -- Periodicals; Systems engineering -- Periodicals; Industrial engineering; Systems engineering; Electronic journals; Periodicals; 670.285
• DOI: 10.1080/24725854.2018.1470357
• Languages: English
• ISSNs: 2472-5854

Abstract

Abstract: Monitoring a large number of units whose health conditions follow complex dynamic evolution is a challenging problem in many healthcare and engineering applications. For instance, a unit may represent a patient in a healthcare application or a machine in a manufacturing process. Challenges mainly arise from: (i) insufficient data collection that results in limited measurements for each unit to build an accurate personalized model in the prognostic modeling stage; and (ii) limited capacity to further collect surveillance measurement of the units in the monitoring stage. In this study, we develop a selective sensing method that integrates prognostic models, collaborative learning, and sensing resource allocation to efficiently and economically monitor a large number of units by exploiting the similarity between them. We showcased the effectiveness of the proposed method using two real-world applications; one on depression monitoring and another with cognitive degradation monitoring for Alzheimer's disease. Comparing with existing benchmark methods such as the ranking-and-selection method, our fully integrated prognosis-driven selective sensing method enables more accurate and faster identification of high-risk individuals.