The Kinect Project: Group motion-based gaming for people living with dementia
Bibliographic record
Abstract
Engaging in enjoyable activities is an essential part of well-being, but people with dementia can find participation increasingly difficult. Motion-based technologies can provide meaningful engagement in a wide range of activities, but for people with dementia to take advantage of these devices requires a good understanding of how best to select and present these activities to this population. The objective of this study was to explore the use of motion-based technology (Xbox Kinect) as a group activity for people with dementia who attend adult day programmes. This qualitative study took place in an adult day programme for older adults with age-related challenges. Participants (n = 23) were observed while playing a digital bowling game presented on Xbox Kinect one hour per week for a period of 20 weeks, to capture naturalistic data. Field notes generated through observations were transcribed and analysed to identify emerging themes. The findings revealed three predominant themes which illustrate the potential of motion-based technology as a group activity for people with dementia who attend adult day programmes: (a) the importance of having a trained trainer, (b) learning versus mastery and (c) playing ‘independently together’. People with dementia can learn to play games presented on motion-based technology and enjoy doing so. Furthermore, using the technology in a group setting fostered an encouraging and supportive environment which further contributed to the leisure experience. However, to be used most effectively, staff must be trained to set-up and interact with the technology, as well as introduce, teach and support people with dementia to use it.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".