Exploring Data Glove and Robotics Hand Exergaming: Lessons Learned
Bibliographic record
Abstract
In this preliminary study, we explore the use of a high-end data glove as a consumer-level hand exergame human interface device. Disorders affecting the musculoskeletal apparatus account for approximately 43 % of all workplace related injuries, leading to increasing claim costs and work absenteeism. Treatment includes unsupervised stretching and exercising with low adherence due to its monotonous and repetitive nature. Exergames, that is the use of games to elicit physical activity, provide engaging experiences that can help motive patients or workers into performing the exercises. Previous works using consumer-level technology have focused on image-based and open electronics 3D printed gloves that have shown the potential of exergames and motion capture as a tool to add immersion. In this paper, we present exergame that employs the Yamaha Data Glove (YDG) integrated to a computer- and robot-based exergame. The data glove allows controlling a virtual arcade crane in addition to interactive sessions with a social robot called ASUS Zenbo Junior. The preliminary quantitative and qualitative data suggest that motion capture data requires further processing and customization to tailor the experience to each user to improve usability and cognitive load affected by suitable tracking hand gestures. The exergame also requires additional cues to ease the experience and maintain users within a state flow.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".