ASTRA Glove: A Wearable Tracking Device for “Accurate Sensing and Tracking of Realtime Articulations”
Bibliographic record
Abstract
Precise tracking of hand movements is essential for applications in robotics, virtual reality, rehabilitation, and human-computer interaction. Wearable devices, such as gloves equipped with inertial measurement units (IMUs), have emerged as a promising solution for capturing detailed hand kinematics. This article introduces ASTRA Glove, an advanced wearable kinematic tracking device that can precisely sense hand movements in real-time. The system consists of 16 IMUs to measure 23 degrees of freedom (DoF) for hand motions. A new Kalman filter-based six-DoF sensor fusion algorithm (SFA) has been developed to provide high levels of precision as well as real-time performance, all while being easy to integrate into practical applications. One feature of this glove is a fast and simple calibration methodology, which allows for accurate tracking of hand movements. In addition, it is affordable, comfortable, lightweight, sturdy, and easy to wear for prolonged periods. It addresses the typical IMU drift problems with a drift reduction technique that substantially enhances its stability and reliability. In addition, a real-time simulator has been created that allows users to visualize the motions of the hands and displays joint positions with high accuracy. The experimental results indicate that ASTRA Glove can achieve higher accuracy than other systems with joint angle errors below 1° and fingertip position error of 1.47 mm in different hand movement 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".