Overview Study of Partially Observable Hidden Markov Models for Ambient Movement Guidance Support
Bibliographic record
Abstract
The study of ambient movement guidance encompasses a multidisciplinary approach to facilitating and guiding individuals, particularly older adults, within their living environments. This involves integration of ambient sensors, such as motion detectors, cameras, or IoT devices, to monitor the movements and activities of individuals in real time. By leveraging these sensors, the system can predict and anticipate the expected movements of the person, allowing for proactive ambient guidance and support. In addition to ambient guidance, robots can also play a role in leading individuals by interfacing through audio prompts or visual cues through their daily activities. However, despite advancements in sensor technology and robotic assistance, uncertainties persist in the monitoring and prediction of movements. These uncertainties can arise from various sources, including sensor noise, occlusions, environmental changes, and inherent variability in human behavior. Addressing these uncertainties requires probabilistic modeling techniques based on partially observable hidden Markov models (POHMMs) and various of its extensions such as POMDP, to effectively capture the dynamic nature of movement patterns and incorporate uncertainty into the decision-making process. This paper presents a detailed overview study of probabilistic framework and how its various interpretation can be used in developing an ambient movement guiding system for supporting individuals, particularly older, in support of ageing-in-place paradigms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".