Assistive robotics and technologies are going to play a vital role in our society. These platforms can support a level of human-robot interaction that is more meaningful, accommodating, and effective. This is especially true in the realms of medicine and rehabilitation, although assistive robots have a wider range of applications. In this work, using a non-intrusive wearable bio-sensor, a PC, and a mobile robot a novel proof of concept system has been developed that can detect human mental and physical states and intervene to promote mental and physical wellbeing. This study has 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. By programming the system to obtain a baseline reading for individual subjects and comparing subsequent sensor values sustained changes in GSR levels due to stress can be detected. Similarly, by utilizing arrays and monitoring changes in accelerometer readings pattern changes associated with different physical activities can be detected. In addition, 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 have been 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 has been given for the system’s ability to recognize human stress and physical state respectively. Additionally, participants have reported a mean of 30.3% reduction of stress and a mean of 23.3% increase in positive mood following the system’s intervention behavior.

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