EMG-based wake gestures eliminate false activations during out-of-set activities of daily living: an online myoelectric control study
Bibliographic record
Abstract
Abstract Objective. While myoelectric control has been commercialized in prosthetics for decades, its adoption for more general human–machine interaction has been slow. Although high accuracies can be achieved across many gestures, current control approaches are prone to false activations in real-world conditions. This is because the same electromyogram (EMG) signals generated during the elicitation of gestures are also naturally activated when performing activities of daily living (ADLs), such as when driving to work or while typing on a keyboard. This can lead the myoelectric control system, which is trained on a closed set of gestures and thus unaware of the muscle activity associated with these ADLs, to be falsely activated, leading to erroneous inputs and user frustration. Approach. To overcome this problem, the concept of wake gestures, whereby users could switch between a dedicated control mode and a sleep mode by snapping their fingers, was explored. Using a simple dynamic time warping model, the real-world user-in-the-loop efficacy of wake gestures as a toggle for myoelectric interfaces was demonstrated through two online ubiquitous control tasks with varying levels of difficulty: (1) dismissing an alarm and (2) controlling a robot. Main results. During these online evaluations, the designed system ignored almost all ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mo>></mml:mo> </mml:mrow> </mml:math> 99.9%) non-target EMG activity generated during a set of ADLs (i.e. walking, typing, writing, phone use, and driving), ignored all control gestures (i.e. wrist flexion, wrist extension, hand open, and hand close), and enabled reliable mode switching during intentional wake gesture elicitation. Additionally, questionnaires revealed that participants responded well to the use of wake gestures and generally preferred false negatives over false positives, providing valuable insights into the future design of these systems. Significance. These results highlight the real-world viability of wake gestures for enabling the intermittent use of myoelectric control, opening up new interaction possibilities for EMG-based inputs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".