LUDO: AN INTERVENTION SYSTEM TO DETER PERSONS WITH MILD DEMENTIA FROM INACTIVITY AND RESTLESSNESS
Bibliographic record
Abstract
The prevalence of dementia is on the rise worldwide. As symptoms of dementia progress, many will experience increased bouts of physical inactivity and restlessness, which will inevitably impair their ability to remain independent in their homes. Existing interventions have incorporated home-based monitoring and/or stimulating activities, however standalone they remain insufficient. In this work, we present ‘LuDo’, which integrates home-based monitoring and stimulating activities in a single working system. LuDo is equipped with two components: (i) A wearable device, and (ii) An interactive stimulating suite. Using advanced machine learning algorithms, LuDo senses an extended period of inactivity or restlessness in persons with dementia (PWD), which triggers the computer to play a familiar sound. Users respond by approaching the periphery of the Kinect camera, activating the TV screen. The screen provides a voice/touch interface for PWD to interact with LuDo. Options include interactive activities and music. LuDo is capable of learning the habits of PWD over time which will recommend content based on user preference, and provides alerts to the carer if the PWD does/ doesn’t respond or engage with the auditory cue. LuDo operates automatically, without user or carer intervention, works passively, activates only when necessary and can be deactivated at any time. An Initial prototype of LuDo is tested on healthy adults and was found to be able to reroute them from their inactive state and engage them in mentally stimulating activities. In future, we plan to conduct similar experiments with PWD and test their level of interaction with LuDo.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".