Gesture recognition using Markov Systems and wearable wireless inertial sensors
Why this work is in the frame
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Bibliographic record
Abstract
Wearable wireless devices and ubiquitous computing are expected to grow significantly in the coming years. Standard inputs such as a mouse and keyboard are not well suited for such mobile systems and gestures are seen as an effective alternative to these classic input styles. This paper examines gesture recognition algorithms that use an inertial sensor worn on the forearm. The recognition algorithms use the sensor's quaternion orientation in either a Hidden Markov Model or Markov Chain based approach. A set of six gestures were selected to fit within the context of an active video game. Despite the fact that the Hidden Markov Model is one of the most commonly used methods for gesture recognition, the experiments showed that the Markov Chain based algorithms outperformed the Hidden Markov Model. The Markov Chain algorithm obtained an average accuracy of 95%, while also having a much faster computation time, making it better suited for real time applications.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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