Yu Enokibori
Papers
1
Total Citations
3
H-Index
1
About
Yu Enokibori is a researcher whose work lies at the intersection of wearable computing, sensor systems, and skill evaluation in manufacturing. His key contributions focus on developing hybrid sensor technologies to assess and quantify human expertise in manual industrial tasks—an area often overlooked in the age of automation. His most cited work, "A method to evaluate metal filing skill level with wearable hybrid sensor" (2012), introduces a novel approach to capturing the nuanced physical movements of expert engineers during metal filing, a craft that remains vital to foundational industries despite widespread robotic automation. By integrating wearable sensors, Enokibori enables objective, data-driven skill assessment, bridging the gap between traditional craftsmanship and modern sensor technology. Though his citation count is modest, his work holds significant practical value for preserving and transferring tacit knowledge in manufacturing. Enokibori’s research exemplifies how wearable sensing can decode expert motor skills, offering pathways for training, quality control, and human-robot collaboration in industrial settings. His contributions are particularly relevant for researchers exploring skill transfer, human performance monitoring, and the future of manual expertise in automated environments.
Research Focus
Key Achievements
Top Papers
- 1