Validation of a piezoelectric sensor array for a wrist-worn muscle-computer interface
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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