Gesture Recognition with Accelerometers for Game Controllers, Phones and Wearables
Bibliographic record
Abstract
Abstract Hidden Markov Models have been effectively used in time series based pattern recognition problems in the past. This work explores using Hidden Markov Models (HMM) to do 3D gesture recognition from accelerometer data. Our work differs from much of the previous work in that we examine the use of discreet HMMs rather than continuous HMMs. An interesting side effect of this is that method is therefore theoretically transportable to other devices that have a 3D sensor output system. In essence this brings us a mechanism to use the HMM model across a series of different sensor devices for gesture recognition. We achieve recognition results with accuracy rates approaching 90 percent for users who are not in the training samples. The speed of our system is also of interest as we are able to classify gestures at a rate of several hundred times per second. As long as the sen-sor system is capable of outputting information about the 3 axes of motion, and the outputs can be discretized to volumetrically equivalent cubic sub-spaces; that information can then be used in this generic model for accurate, high speed gesture recognition.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".