  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.
