A Quantitative Analysis of Activities of Daily Living: Insights into Improving Functional Independence with Assistive Robotics
It addresses the problem of prioritizing assistive robotics development for the elderly and disabled by quantifying real-world ADLs, but it is incremental as it builds on existing data without introducing new methods.
This study analyzed Activities of Daily Living (ADL) using lifelogging databases to compute task frequency from video and IoT sensor data, aiming to bridge gaps between robotics and health research by developing a robotics-relevant taxonomy.
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.