lightbulbPractical problemEmbedded Deployment, Automation Integration, and Device Constraints
How operators affect manufacturing performance still lacks fine-grained quantitative analysis.Smart Devices, Sensing Hardware, and Embedded Interaction / Embedded Deployment, Automation Integration, and Device Constraints
How operators affect manufacturing performance still lacks fine-grained quantitative analysis.
Similar questions
lightbulbPractical problemEmbedded Deployment, Automation Integration, and Device Constraints
Viewers struggle to quickly extract useful knowledge from chaotic danmu when watching science videos.lightbulbPractical problemEmbedded Deployment, Automation Integration, and Device Constraints
Airports often face integration difficulties and implementation delays when adopting automation technology.lightbulbPractical problemEmbedded Deployment, Automation Integration, and Device Constraints
Limited device performance makes it difficult to balance efficient algorithms and real-time response in UX.lightbulbPractical problemEmbedded Deployment, Automation Integration, and Device Constraints
Woodworkers and ordinary users cannot easily fabricate and use smart furniture.lightbulbPractical problemEmbedded Deployment, Automation Integration, and Device Constraints
Drones are limited by batteries; last-mile delivery range is small and costly.UbiComp '21Efficient Scheduling of Energy-Constrained Drones for Last-Mile Package Delivery Using Crowdsourced Buses
lightbulbPractical problemEmbedded Deployment, Automation Integration, and Device Constraints
Qualitative researchers analyze audiovisual data inefficiently and easily miss key details.Related papers
CHI 2025
CoKnowledge: Supporting Assimilation of Time-synced Collective Knowledge in Online Science Videos
Yuanhao Zhang, Yumeng Wang, Xiyuan Wang
CHI 2025
Why does Automation Adoption in Organizations Remain a Fallacy?: Scrutinizing Practitioners' Imaginaries in an International Airport
Garoa Gomez-Beldarrain, Himanshu Verma, Euiyoung Kim
CHI 2024
Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences
Fred Hohman, Mary Beth Kery, Donghao Ren
CHI 2024
Tagnoo: Enabling Smart Room-Scale Environments with RFID-Augmented Plywood
Yuning Su, Tingyu Zhang, Jiuen Feng
CHI 2023
Charlie and the Semi-Automated Factory: Data-Driven Operator Behavior and Performance Modeling for Human-Machine Collaborative Systems
Eunji Park, Yugyeong Jung, Inyeop Kim
CHI 2021
From Detectables to Inspectables: Understanding Qualitative Analysis of Audiovisual Data
Krishna Subramanian, Johannes Maas, Jan Borchers
CHI 2021
A Right Time to Give: Beyond Saving Time in Automated Conditional Donations
Nicola J Bidwell, Chris Elsden, Ludwig Trotter