Gesture Recognition with Accelerometers for Game Controllers, Phones and Wearables
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
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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