Enhancing Virtual and Augmented Reality Interactions with a MediaPipe-Based Hand Gesture Recognition User Interface
Bibliographic record
Abstract
Interaction with virtual objects is crucial for presence in Virtual Reality (VR) and Augmented Reality (AR) applications.However, controllers are still predominantly used for operations in virtual spaces.Hand gestures offer a more intuitive approach than keyboards and mice for interactions in these environments.In previous research, hand motion classification was implemented using only simple heuristics.This study introduces a User Interface (UI) employing MediaPipe and artificial intelligence to utilize hand gestures as an input device.Unlike the previous research, which could only identify one gesture, the current implementation successfully classifies three gestures with 95.4% accuracy: pointer, pick, and fist.Efforts were made to optimize the process, including the examination of multi-threading and PyWin32.While multi-threading did not yield significant improvements, the use of PyWin32 resulted in approximately three times higher Frames Per Second (FPS) compared to the absence of PyWin32.Further gestures can potentially be added to enhance the system's capabilities.This line of research has potential applications in diverse fields such as gaming, simulation, rehabilitation, and smart home technology.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".