Enhancing Virtual and Augmented Reality Interactions with a MediaPipe-Based Hand Gesture Recognition User Interface
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it