The Fifth Main Dynamic Factor: Skin Temperature and Its Effects on EMG-Based Gesture Recognition
Bibliographic record
Abstract
Surface electromyography (sEMG) has become a cornerstone for gesture recognition in prosthetic control and human-machine interfaces. However, its performance is often compromised by several dynamic factors, including gesture intensity, limb position, electrode shift, and signal non-stationarities. This work introduces and investigates skin temperature as the "fifth" dynamic factor influencing sEMG-based gesture recognition. Synchronized sEMG, photoplethys-mography (PPG), and inertial measurement unit (IMU) data were recorded from 12 participants without limb differences performing five hand/wrist gestures under three temperature conditions (cold, baseline, and hot) using the BioPoint wearable device. Analysis revealed that cold conditions increased the mean absolute value and reduced the median frequency of sEMG signals, while the signal's complexity (assessed via fuzzy entropy) remained largely unchanged. Gesture recognition models trained exclusively on baseline data showed a decline in accuracy when tested on non-baseline temperature conditions. In contrast, training with temperature-diverse data improved classification robustness across thermal conditions, albeit at the cost of baseline performance. Furthermore, while a multisensor approach combining EMG, PPG, and IMU data enhanced baseline accuracy, it also demonstrated heightened sensitivity to temperature variations. These findings underscore the necessity for skin temperature-aware strategies for the development of robust sEMG-based gesture recognition systems. Additionally, we introduce a novel hand gesture dataset collected under varying skin temperature conditions to support future research on developing more adaptive and reliable gesture recognition solutions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".