Finger Tracking for Human Computer Interface Using Multiple Sensor Data Fusion
Bibliographic record
Abstract
As human-computer interface advances from two to three dimensions, new input devices are required to allow low-cost learning and increased human involvement in virtual reality contexts. In this study, we offer a small, finger-worn, wireless motion-tracking platform that can be reprogrammed for a number of functions to enhance mobile computing. First, it functions as a wireless mouse in the air without requiring a surface for a mouse or trackpad, making it appropriate for augmented-reality or virtual-reality systems. Second, it supports single-finger motion tracking in its entirety. A more complex version is able to mix information from more units on multiple fingers to create a greater variety of options. The prototype of our ring will have an ultra-compact wireless sensing platform with an on-board triaxial accelerometer, triaxial magnetometer, triaxial gyroscope, and a short-range wireless Bluetooth transmitter with a ToF sensor for finger flexion detection. Quantitative and qualitative evaluations of the accuracy and usefulness of the IMU sensor breakout board have been conducted. The results demonstrate that the IMU sensor finger tracking system is intuitive for mouse-like tasks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 0.001 |
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".