An overview of Markov Chains for monitoring indoor movements and ambient motion detection
Shahram Payandeh PhD PEng*
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AbstractBackground: In recent years and due to the availability of non-wearable sensing technologies, there has been a widespread interest in developing methods for monitoring the movements of older adults and further estimating any anomalies.
Objective: Low-cost motion detection has been gaining particular attention due to its widespread availability, ease of deployment, and inherent property in protecting privacy. In addition, and due to the cost-benefits, it has become an attractive alternative for the low-income sector of society. This paper presents an overview of a monitoring framework and a review of the literature using motion detection sensors.
Method: Markov Chains (MC) have been explored by many researchers as a suitable framework for monitoring and estimating sequences of events associated with movements and activities which can further be used as a part of an anomaly detection system in a sensor network. This paper presents an overview of this method and related literature.
Results: A brief overview of MC and the related literature with some insights and challenges associated with the potential limitations and future extensions. One of the challenges of utilizing MC is the definition of what can be considered the state of movements and activities and what can be used as a measure of such state. In this context, various extensions of MC have been utilized where the state of the system can not be measured directly and are defined in a form of Hidden Markov Models (HMM).
Conclusion: Proper deployment of motion detection sensors and associated MC for monitoring requires an in-depth understanding and co-designing of the system with the family members, care providers, and engineers in order to fully take advantage of such technology. Depending on the number and properties of the selected sensors (such as their effective range and the inherent time delay), particular attention needs to be paid to what can constitute a measurable (observable) state and what can be further defined as a hidden state.Keywords: Markov Chains (MC), movement monitoring, ambient motion detection, Hidden Markov Model (HMM)
Shahram Payandeh PhD PEng* (2022). An overview of Markov Chains for monitoring indoor movements and ambient motion detection. Gerontechnology, 21(1), 1-11
https://doi.org/10.4017/gt.2022.21.1.590.07