Validation of a piezoelectric sensor array for a wrist-worn muscle-computer interface
Bibliographic record
Abstract
The average adult spends more hours per day interacting with a computer than sleeping. Computer interfaces that require low physical effort offer users a heathy and efficient interaction method. The lowest physical effort device is the brain-computer interface, which uses electric signals on the scalp. However, since electroencephalography signals are difficult to detect and process, we are investigating the use of alternative biosignals suitable for wearable computer interfaces. Sensors worn over or near muscles can detect electromyographic or mechanomyographic signals, where the latter refer to vibrations and pressure changes caused by muscle activation. Previously, mechanomyographic signals have been measured using accelerometers, microphones and other vibration sensing equipment, and some wearable computer interfaces based on muscle activation have been investigated. We are instead using piezo-electric sensors to measure vibration and pressure, as they are inexpensive, small and highly sensitive. Using piezo-electric sensors, we developed a wrist wearable sensor array that allowed unrestricted movement of the fingers and produced a recordable signal. The movement generated signals were recorded during experiments involving small individual finger movements. Each isolated movement was associated with a single recording which was analysed for a variety of signal features, correlation with the movement, and repeatability between sessions. The correlation and repeatability results support the use of piezo-electric sensors as a viable wearable computer interface sensor. Such a device could be used for prosthetic control, robot assisted surgery, and mobile computer interaction.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".