A Transformer-Based Approach for Better Hand Gesture Recognition
Bibliographic record
Abstract
Hand gesture recognition (HGR) is a vital area of research with widespread applications and employing disruptive technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT). HGR is very important in human-computer interaction (HCI), especially for people with disabilities. Several approaches have been described in the literature, including image-based, radar-based, and haptic-based ones. However, these approaches are difficult to implement in real time as they require high processing time (PT). Inertial sensors (IS) offer a worthy alternative for HGR and require much less PT. Several research papers have been published in this domain. However, models capturing complex temporal patterns using traditional machine learning (ML) algorithms have limitations. In this paper, we propose a transformer-based approach to accurately recognize dynamic gestures. A dataset we collected from accelerometer (ACC) and gyroscope (GYR) sensors was used to evaluate the performance of our approach. Our experimental results achieved an accuracy of 96.77% and outperformed traditional ML algorithms in terms of accuracy using the same dataset: Linear Discriminant Analysis (LDA) with 58.06%, KNearest Neighbor (KNN) with 70.97%, XGBoost (XGB) with 74.19%, Convolutional neural network (CNN) with 74.19%, Random Forest (RF) with 77.42%, and Support vector machine (SVM) with 93.55%. Furthermore, we implemented a real-time HGR system that can achieve a very short response time of less than 300 ms.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.005 |
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".