  Human assistive robotics have the potential to help the elderly and
individuals living with disabilities with their Activities of Daily Living
(ADL). Robotics researchers present bottom up solutions using various control
methods for different types of movements. Health research on the other hand
focuses on clinical assessment and rehabilitation leaving arguably important
differences between the two domains. In particular, little is known
quantitatively on what ADLs humans perform in their everyday environment - at
home, work etc. This information can help guide 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 different terms and
taxonomies for representing tasks and motions. We derive and discuss a
robotics-relevant taxonomy from this quantitative ADL task and ICF motion data
in attempt to ameliorate these taxonomic differences. Our statistics quantify
that humans reach, open drawers, doors, and retrieve and use objects hundreds
of times a day. Commercial wheelchair mounted robot arms can help 150,000 upper
body disabled in the USA alone, but only a few hundred robots are deployed.
Better user interfaces, and more capable robots can increase the potential user
base and number of ADL tasks solved significantly.
