A Quaternion-Based Motion Tracking and Gesture Recognition System Using Wireless Inertial Sensors
Bibliographic record
Abstract
This work examines the development of a unified motion tracking and gesture recognition system that functions through worn inertial sensors.The system is comprised of a total of ten wireless sensors and uses their quaternion output to map the player's motions to an onscreen character in real-time.To demonstrate the capabilities of the system, a simple virtual reality game was created.A hierarchical skeletal model was implemented that allows players to navigate the virtual world without the need of a handheld controller.In addition to motion tracking, the system was also tested for its potential for gesture recognition.A sensor on the right forearm was used to test six different gestures, each with 500 training samples.Despite the widespread use of Hidden Markov Models for recognition, our modified Markov Chain algorithm obtained higher average accuracies at 95%, as well as faster computation times.This makes it an ideal candidate for use in real time applications.Combining motion tracking and dynamic gesture recognition into a single unified system is unique in the literature and comes at a time when virtual reality and wearable computing are emerging in the marketplace.vi 6.4 Methods .....
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".