Predicating movement disorders using non-verbals

Design team interactions are one of the least understood aspects of the engineering design process. Given the integral role that designers play in the engineering design process, understanding the emotional states of individual design team members will help us quantify interpersonal interactions and how those interactions affect resulting design solutions. The methodology presented in this paper enables automated detection of individual team member’s emotional states using non-wearable sensors. The methodology uses the link between body language and emotions to detect emotional states with accuracies above 98%. The practical implications include that machine learning to detect emotions using non-invasive sensors in design teams and that there was a high accuracy over 90% achieved for detecting many body language poses.

Behoora, I., & Tucker, C. S. (2015). Machine learning classification of design team members’ body language patterns for real time emotional state detection. Design Studies, 39, 100-127.

 

Research Contact:
Conrad Tucker, PhD
ctucker4@psu.edu

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