Date of Award
Master of Science
Carl K Chang
Activities of Daily Living (ADL) can give us information about an individual’s health, both physical and mental. They are captured using sensors and then processed and recognized into different activities. Activity recognition is the process of understanding a person’s movement and actions. In this work, we develop a language in a simple grammar that describes the activity and uses it to recognize the activity. We call this language as Activities of Daily Living Description Language, or A(DL)2 in short.Even after an activity has been recognized, the data it represents is still digital data and it would take some expertise and time to understand it. To overcome this problem, a system that can visualize and animate individuals’ activity in real time without violating any privacy issues, can be built. This will not only help in understanding the current state of individual but will also help those who are in charge of monitoring them remotely like nurses, doctors, family members, thereby rendering better care and support especially to the elderly people who are aging. We propose a real time activity recognition and animation system that recognizes and animates the individual’s activity. We experimented with one of the basic ADLs, walking, and found the result satisfactory. Individuals location is tracked using sensors and is sent to the recognition system which then decides the type of activity in real time by using the language to describe it, and then the data is sent to a visualization system which animates that activity. When fully developed, this system intends to serve the purpose of providing better health care and immediate support to the people in need.
Mohammed Shaiqur Rahman
Rahman, Mohammed Shaiqur, "Activity recognition and animation of activities of daily living" (2020). Graduate Theses and Dissertations. 18383.