Deep Learning Technique in Recognizing Hand Grasps using FMG signals
Bibliographic record
Abstract
Grasping objects are common phenomenon in daily human activities. Force myography (FMG) signal, a noninvasive technique can record muscle movements while a human participant grasps different objects and be categorized using machine learning (ML) algorithms. In this paper, a popular deep learning technique is presented for hand grasp recognition. A novel convolutional neural network (CNN) architecture was implemented in learning grasps via force myography. Twelve participants wearing an FMG band on dominant hand's forearm performed six hand grasps. Training dataset consisted of one-handed grasping small objects of different shapes and sizes either wrapping or pinching with fingers with a variety of arm poses. The proposed FMG-based CNN model obtained cross-trial classification accuracy of 96% (population mean) and was found comparable with other ML techniques. Pretranined Alexnet (with ImageNet dataset) through transfer learning was implemented to classify the hand grasps for comparison. The proposed model outperformed the pretrained Alexnet in terms of validation accuracy, loss, and training time. For future FMG-based practical applications, it would be advantageous to use the model for transfer learning where comparatively smaller datasets are desirable for training purpose.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".