On the use of peripheral autonomic signals for binary control of body–machine interfaces
Bibliographic record
Abstract
In this work, the potential of using peripheral autonomic (PA) responses as control signals for body-machine interfaces that require no physical movement was investigated. Electrodermal activity, skin temperature, heart rate and respiration rate were collected from six participants and hidden Markov models (HMMs) were used to automatically detect when a subject was performing music imagery as opposed to being at rest. Experiments were performed under controlled silent conditions as well as in the presence of continuous and startle (e.g. door slamming) ambient noise. By developing subject-specific HMMs, music imagery was detected under silent conditions with the average sensitivity and specificity of 94.2% and 93.3%, respectively. In the presence of startle noise stimuli, the system sensitivity and specificity levels of 78.8% and 80.2% were attained, respectively. In environments corrupted by continuous ambient and startle noise, the system specificity further decreased to 75.9%. To improve the system robustness against environmental noise, a startle noise detection and compensation strategy were proposed. Once in place, performance levels were shown to be comparable to those observed in silence. The obtained results suggest that PA signals, combined with HMMs, can be useful tools for the development of body-machine interfaces that allow individuals with severe motor impairments to communicate and/or to interact with their environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 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".