2021/04/08 by Laura Petrich, Jun Jin, Petrich, Laura +5 · 2 citations
Computer Science · Medicine · Social Sciences · #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Robotics (cs.RO) #Technology Use by Older Adults #Tracheal and airway disorders
paper · pdf · doi:10.48550/arxiv.2104.03892
openalex publication_date 2021/04/08 · openalex created_date 2022/07/15 · openalex updated_date 2026/07/28
Human assistive robotics have the potential to help the elderly and individuals living with disabilities with their Activities of Daily Living (ADL). Robotics researchers focus on assistive tasks from the perspective of various control schemes and motion types. Health research on the other hand focuses on clinical assessment and rehabilitation, arguably leaving important differences between the two domains. In particular, little is known quantitatively on which ADLs are typically carried out in a persons everyday environment - at home, work, etc. Understanding what activities are frequently carried out during the day can help guide the development and prioritization of robotic technology for in-home assistive robotic deployment. This study targets several lifelogging databases, where we compute (i) ADL task frequency from long-term low sampling frequency video and Internet of Things (IoT) sensor data, and (ii) short term arm and hand movement data from 30 fps video data of domestic tasks. Robotics and health care communities have differing terms and taxonomies for representing tasks and motions. In this work, we derive and discuss a robotics-relevant taxonomy from quantitative ADL task and motion data in attempt to ameliorate taxonomic differences between the two communities. Our quantitative results provide direction for the development of better assistive robots to support the true demands of the healthcare community.