A Real-Time Robotic Movement Control System Based on Hand Gesture Recognition Using Human Computer Interaction (HCI) Principles
Bibliographic record
Abstract
This paper presents a real-time robotic movement control system (RMCS) utilizing hand gesture recognition through a Cascaded CNN-SVM approach. The system was designed to enhance human-robot interaction by allowing users to control RMs seamlessly through gestures. Extensive testing was conducted on a dataset comprising various gestures, achieving an impressive overall recognition accuracy of 93.6%. Specific gestures, such as “Wave” and “Point,” demonstrated recognition accuracies of 95.2% and 93.8%, respectively. The system was also evaluated for response times, with an average response latency of 125 ms, ensuring efficient real-time operation. User feedback highlighted high levels of satisfaction, with average ratings of 4.4 for intuitiveness and 4.5 for the likelihood to recommend the system. These results indicate that the Cascaded CNN-SVM model is effective in recognizing gestures with high precision while maintaining low latency, making it suitable for practical applications in RMCS.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".