An overview of Markov Chains for monitoring indoor movements and ambient motion detection
Bibliographic record
Abstract
Background: 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".