Towards the use of consumer-grade electromyographic armbands for interactive, artistic robotics performances
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
In recent years, gesture-based interfaces have been explored in order to control robots in non-traditional ways. These require the use of systems that are able to track human body movements in 3D space. Deploying Mo-cap or camera systems to perform this tracking tend to be costly, intrusive, or require a clear line of sight, making them ill-adapted for artistic performances. In this paper, we explore the use of consumer-grade armbands (Myo armband) which capture orientation information (via an inertial measurement unit) and muscle activity (via electromyography) to ultimately guide a robotic device during live performances. To compensate for the drop in information quality, our approach rely heavily on machine learning and leverage the multimodality of the sensors. In order to speed-up classification, dimensionality reduction was performed automatically via a method based on Random Forests (RF). Online classification results achieved 88% accuracy over nine movements created by a dancer during a live performance, demonstrating the viability of our approach. The nine movements are then grouped into three semantically-meaningful moods by the dancer for the purpose of an artistic performance achieving 94% accuracy in real-time. We believe that our technique opens the door to aesthetically-pleasing sequences of body motions as gestural interface, instead of traditional static arm poses.
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.
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 | 0.001 |
| Open science | 0.001 | 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