User Training with Error Augmentation for Electromyogram-based Gesture Classification
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909147274936320 |
|---|---|
| author | Bicer, Yunus Smedemark-Margulies, Niklas Celik, Basak Sunger, Elifnur Orendorff, Ryan Naufel, Stephanie Imbiriba, Tales Erdoğmuş, Deniz Tunik, Eugene Yarossi, Mathew |
| author_facet | Bicer, Yunus Smedemark-Margulies, Niklas Celik, Basak Sunger, Elifnur Orendorff, Ryan Naufel, Stephanie Imbiriba, Tales Erdoğmuş, Deniz Tunik, Eugene Yarossi, Mathew |
| contents | We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wrist-band configuration. sEMG data were streamed into a machine-learning algorithm that classified hand gestures in real-time. After an initial model calibration, participants were presented with one of three types of feedback during a human-learning stage: veridical feedback, in which predicted probabilities from the gesture classification algorithm were displayed without alteration, modified feedback, in which we applied a hidden augmentation of error to these probabilities, and no feedback. User performance was then evaluated in a series of minigames, in which subjects were required to use eight gestures to manipulate their game avatar to complete a task. Experimental results indicated that, relative to baseline, the modified feedback condition led to significantly improved accuracy and improved gesture class separation. These findings suggest that real-time feedback in a gamified user interface with manipulation of feedback may enable intuitive, rapid, and accurate task acquisition for sEMG-based gesture recognition applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_07289 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | User Training with Error Augmentation for Electromyogram-based Gesture Classification Bicer, Yunus Smedemark-Margulies, Niklas Celik, Basak Sunger, Elifnur Orendorff, Ryan Naufel, Stephanie Imbiriba, Tales Erdoğmuş, Deniz Tunik, Eugene Yarossi, Mathew Human-Computer Interaction Machine Learning Signal Processing We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wrist-band configuration. sEMG data were streamed into a machine-learning algorithm that classified hand gestures in real-time. After an initial model calibration, participants were presented with one of three types of feedback during a human-learning stage: veridical feedback, in which predicted probabilities from the gesture classification algorithm were displayed without alteration, modified feedback, in which we applied a hidden augmentation of error to these probabilities, and no feedback. User performance was then evaluated in a series of minigames, in which subjects were required to use eight gestures to manipulate their game avatar to complete a task. Experimental results indicated that, relative to baseline, the modified feedback condition led to significantly improved accuracy and improved gesture class separation. These findings suggest that real-time feedback in a gamified user interface with manipulation of feedback may enable intuitive, rapid, and accurate task acquisition for sEMG-based gesture recognition applications. |
| title | User Training with Error Augmentation for Electromyogram-based Gesture Classification |
| topic | Human-Computer Interaction Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2309.07289 |