Interactive Hand Gesture Control System for Augmented Reality-based Games
Bibliographic record
Abstract
This research paper presents the development of an innovative 2D augmented reality (AR) car game utilizing Python, OpenCV, and Pygame. The primary objective of the game is to provide an interactive platform for individuals with motor skill challenges, such as Parkinson’s disease, to enhance their hand and gesture control abilities. The game involves controlling a virtual car superimposed onto the real-world environment using an AR marker. Hand gestures captured by a webcam control the car's movements, creating a novel and accessible gaming experience. The implementation incorporates computer vision techniques from the OpenCV library to detect an AR marker, interpret hand gestures, and map them to specific actions within the game. The Pygame library facilitates the creation of an engaging gaming environment, where users can employ intuitive hand movements to navigate the virtual car. The system's performance is evaluated in varying lighting conditions, and the results demonstrate a promising accuracy rate in gesture detection. The limitations and potential improvements are discussed, emphasizing the system's application in assisting individuals with motor impairments and its broader implications for human-computer interaction. This research provides insights into the intersection of augmented reality, computer vision, and assistive technology, offering a foundation for further advancements in accessible gaming experiences. Keywords: Augmented Reality, Hand Gesture Control, Computer Vision, Game Interaction, Assistive Technology
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".