A Scalable Vision-Based Method for Motion Assessment of Full-Scale Motorized Hospital Beds Using ArUco Markers

Ariq Naufal Rabbani¹Cheng-Wei Chen²Radon Dhelika^1*

• ¹University of Indonesia, Depok, Indonesia
• ²national taiwan university, Taipei, Taiwan

This study explores the development and performance evaluation of motorized hospital beds, which aim to alleviate the physical burden on healthcare workers and improve hospital efficiency. Traditional hospital beds, which require significant physical effort to maneuver, often lead to musculoskeletal injuries among healthcare workers. The proposed solution involves implementing mobile robot technology, specifically swerve drive systems, into conventional hospital beds to enhance their mobility. Two prototypes of motorized hospital beds were developed, each featuring different swerve drive configurations. Prototype 1 utilizes DC motors with chain-driven wheels for propulsion and a gear-based steering system. Prototype 2 employs a pulley and belt mechanism for propulsion and a servo motor for steering. Both prototypes underwent comprehensive motion testing, including straight-line and rotational motion tests, using ArUco markers for precise position and movement measurements. Results demonstrated that Prototype 2 significantly outperformed Prototype 1 in maintaining a straight trajectory, with an average deviation of 5.38 cm compared to 32.68 cm for Prototype 1. Factors influencing these results included initial positioning, wheel grip, and acceleration balance. The study concludes that the ArUco marker vision-based method is a reliable tool for measuring hospital bed movements. Prototype 2's enhanced straight-line motion capability highlights its potential to reduce physical strain on healthcare workers. Further optimization and addressing mechanical and environmental factors can lead to the development of more efficient motorized hospital beds, ultimately improving hospital operations and patient care.