Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.04241 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911869751525376 |
|---|---|
| author | Garcia-Sosa, Alejandro Quintana-Hernandez, Jose J. Ballester, Miguel A. Ferrer Carmona-Duarte, Cristina |
| author_facet | Garcia-Sosa, Alejandro Quintana-Hernandez, Jose J. Ballester, Miguel A. Ferrer Carmona-Duarte, Cristina |
| contents | Sensors and Artificial Intelligence (AI) have revolutionized the analysis of human movement, but the scarcity of specific samples presents a significant challenge in training intelligent systems, particularly in the context of diagnosing neurodegenerative diseases. This study investigates the feasibility of utilizing robot-collected data to train classification systems traditionally trained with human-collected data. As a proof of concept, we recorded a database of numeric characters using an ABB robotic arm and an Apple Watch. We compare the classification performance of the trained systems using both human-recorded and robot-recorded data. Our primary objective is to determine the potential for accurate identification of human numeric characters wearing a smartwatch using robotic movement as training data. The findings of this study offer valuable insights into the feasibility of using robot-collected data for training classification systems. This research holds broad implications across various domains that require reliable identification, particularly in scenarios where access to human-specific data is limited. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04241 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring the Potential of Robot-Collected Data for Training Gesture Classification Systems Garcia-Sosa, Alejandro Quintana-Hernandez, Jose J. Ballester, Miguel A. Ferrer Carmona-Duarte, Cristina Robotics Artificial Intelligence Sensors and Artificial Intelligence (AI) have revolutionized the analysis of human movement, but the scarcity of specific samples presents a significant challenge in training intelligent systems, particularly in the context of diagnosing neurodegenerative diseases. This study investigates the feasibility of utilizing robot-collected data to train classification systems traditionally trained with human-collected data. As a proof of concept, we recorded a database of numeric characters using an ABB robotic arm and an Apple Watch. We compare the classification performance of the trained systems using both human-recorded and robot-recorded data. Our primary objective is to determine the potential for accurate identification of human numeric characters wearing a smartwatch using robotic movement as training data. The findings of this study offer valuable insights into the feasibility of using robot-collected data for training classification systems. This research holds broad implications across various domains that require reliable identification, particularly in scenarios where access to human-specific data is limited. |
| title | Exploring the Potential of Robot-Collected Data for Training Gesture Classification Systems |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2405.04241 |