Motion Sensor–Based Fall Prevention for Senior Care: A Hidden Markov Model with Generative Adversarial Network Approach

Shuo Yu (), Yidong Chai (), Sagar Samtani (), Hongyan Liu () and Hsinchun Chen ()
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Shuo Yu: Area of Information Systems and Quantitative Sciences, Rawls College of Business, Texas Tech University, Lubbock, Texas 79409
Yidong Chai: Department of Electronic Commerce, School of Management, Hefei University of Technology, Hefei, Anhui 230009, China; Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management, Hefei, Anhui 230009, China; Philosophy and Social Sciences Laboratory of Data Science and Smart Society Governance, Ministry of Education, Hefei, Anhui 230009, China
Sagar Samtani: Department of Operations and Decision Technologies, Kelley School of Business, Indiana University, Bloomington, Indiana 47405
Hongyan Liu: Department of Management Science and Engineering, School of Economics and Management, Tsinghua University, Beijing 100084, China
Hsinchun Chen: Department of Management Information Systems, Eller College of Management, University of Arizona, Tucson, Arizona 85721

Information Systems Research, 2024, vol. 35, issue 1, 1-15

Abstract: Whereas modern medicine has enabled humans to live longer and more robust lives, recent years have seen a significant increase in chronic care costs. The prevention of threats to mobility, such as falls, freezing of gait, and others, is critical for chronic disease management. Researchers and physicians often analyze data from wearable motion sensor–based information systems (IS) to prevent falls because of their convenience, low cost, and user privacy protection. However, prior studies on fall prevention often achieve suboptimal performance because of their limited capacities in modeling data distributions. In this study, we adopt the computational design science paradigm to develop a novel fall prevention framework, which includes the hidden Markov model with generative adversarial network (HMM-GAN) that extracts temporal and sequential patterns from sensor signals and recognizes snippet states, and a logistic regression that utilizes the snippet states and determines whether and when to trigger protective devices to prevent fall injuries. Drawing upon the HMM, deep learning, and a new expectation-maximization instantiation, the proposed framework addresses limitations of existing methods by automatically extracting features from motion sensor data, accounting for both independent and sequential information in data snippets, operating on sensor signals with varying distributions and sharp peaks and valleys, allowing lead times, and being applicable in both semisupervised and supervised modes. We evaluate the proposed fall prevention framework against prevailing fall prevention models and the HMM-GAN component against state-of-the-art sensor analytics models on selected large-scale ground truth data sets containing thousands of falls and normal activities. Through an in-depth case study, we demonstrate how the proposed framework can lead to significantly reduced potentially catastrophic falls by senior citizens and produce more than $33 million of economic benefits over competing models. Besides practical health information technology contributions, HMM-GAN offers methodological contributions to the IS knowledge base for scholars designing novel information technology artifacts for healthcare applications.

Keywords: computational design science; healthcare predictive analytics; fall prevention; motion sensors; hidden Markov model; generative adversarial networks (search for similar items in EconPapers)
Date: 2024
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