Term of Award
Master of Science in Applied Engineering (M.S.A.E.)
Document Type and Release Option
Thesis (restricted to Georgia Southern)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department of Mechanical Engineering
Committee Member 1
Committee Member 2
Assistive robotics and technologies are going to play a vital role in our society's future. These platforms allow for a level of human-robot interaction that is more meaningful, safer, and productive. This is especially true in the realms of medicine and rehabilitation, although assistive robots have a wide range of applications. By using a non-intrusive wearable bio-sensor, a PC, and a mobile robot a novel proof of concept system was developed that can detect human mental and physical states and intervene to promote mental and physical well being. This study utilized a skin-conductivity sensor to monitor changes in galvanic skin response (GSR) due to the presence of stress or anxiety along with a three-axis accelerometer to detect changes in physical activity levels. Two data processing algorithms have been developed to identify the mental and physical states by employing trend analysis techniques. Behaviors and motions aimed at alleviating human mental stress and physical inactivity have been developed by employing distraction and reminder intervention methods using a mobile robot. Experiments were conducted on human subjects to evaluate the proposed robotic system’s capability to identify mental and physical states and intervene to improve their situation through participant responses. Based on the responses, a mean rating of 4.41 and 4.83 out of 5 was given for the system’s ability to recognize human stress and physical state respectively. Additionally, participants reported a mean of a 30.3% reduction of stress and a mean 23.3% increase in mood following the system’s intervention behavior.
Lansing, Kyle, "A Non-intrusive Wearable Bio-sensor Based Assistive Robotic System for Human Mental and Physical Intervention" (2017). Electronic Theses & Dissertations. 1567.
Research Data and Supplementary Material