Hand gesture recognition using force myography of the forearm activities and optimized features
Why this work is in the frame
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Bibliographic record
Abstract
Hand gesture recognition has emerged as an attractive and promising method in human-machine interaction. Applying the simple, efficient and inexpensive devices is necessary for this kind of the application. In this paper, we propose a novel hand gesture recognition by investigating the forearm muscles movement data processing sensed by an array of 8 Force Sensor Resistor (FSR). The acquired data is sent by the wireless device to the processing unit that is more convenient for users. The feature extraction scheme is proposed to get some useful information about the data. A graph of the FSR sensed signals are constructed by considering the extracted features from them. Weights of the graph edge are computed by finding the differences between correspondents pair of sensors. Multiobjective optimization is applied to find the optimum parameters and providing the best description of the sensors' relations in each class. The proposed approach will be evaluated by conducting experiments. 10 volunteered persons participated in gathering FMG data in different classes of the hand gestures. As the results show the proposed method has good performance to recognize hand gestures and has almost 93% accuracy in the overall.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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