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Smart Devices, Sensing Hardware, and Embedded Interaction / Embedded Deployment, Automation Integration, and Device Constraints

Drones are limited by batteries; last-mile delivery range is small and costly.

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

Similar questions

lightbulbPractical problemEmbedded Deployment, Automation Integration, and Device Constraints

Viewers struggle to quickly extract useful knowledge from chaotic danmu when watching science videos.

CHI '25CoKnowledge: Supporting Assimilation of Time-synced Collective Knowledge in Online Science Videos

lightbulbPractical problemEmbedded Deployment, Automation Integration, and Device Constraints

Airports often face integration difficulties and implementation delays when adopting automation technology.

CHI '25Why does Automation Adoption in Organizations Remain a Fallacy?: Scrutinizing Practitioners' Imaginaries in an International Airport

lightbulbPractical problemEmbedded Deployment, Automation Integration, and Device Constraints

Limited device performance makes it difficult to balance efficient algorithms and real-time response in UX.

CHI '24Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences

lightbulbPractical problemEmbedded Deployment, Automation Integration, and Device Constraints

Woodworkers and ordinary users cannot easily fabricate and use smart furniture.

CHI '24Tagnoo: Enabling Smart Room-Scale Environments with RFID-Augmented Plywood

lightbulbPractical problemEmbedded Deployment, Automation Integration, and Device Constraints

How operators affect manufacturing performance still lacks fine-grained quantitative analysis.

CHI '23Charlie and the Semi-Automated Factory: Data-Driven Operator Behavior and Performance Modeling for Human-Machine Collaborative Systems

lightbulbPractical problemEmbedded Deployment, Automation Integration, and Device Constraints

Qualitative researchers analyze audiovisual data inefficiently and easily miss key details.

CHI '21From Detectables to Inspectables: Understanding Qualitative Analysis of Audiovisual Data

Related papers

CoKnowledge: Supporting Assimilation of Time-synced Collective Knowledge in Online Science Videos

Yuanhao Zhang, Yumeng Wang, Xiyuan Wang

Why does Automation Adoption in Organizations Remain a Fallacy?: Scrutinizing Practitioners' Imaginaries in an International Airport

Garoa Gomez-Beldarrain, Himanshu Verma, Euiyoung Kim

Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences

Fred Hohman, Mary Beth Kery, Donghao Ren

Tagnoo: Enabling Smart Room-Scale Environments with RFID-Augmented Plywood

Yuning Su, Tingyu Zhang, Jiuen Feng

Charlie and the Semi-Automated Factory: Data-Driven Operator Behavior and Performance Modeling for Human-Machine Collaborative Systems

Eunji Park, Yugyeong Jung, Inyeop Kim

From Detectables to Inspectables: Understanding Qualitative Analysis of Audiovisual Data

Krishna Subramanian, Johannes Maas, Jan Borchers

A Right Time to Give: Beyond Saving Time in Automated Conditional Donations

Nicola J Bidwell, Chris Elsden, Ludwig Trotter