Date of Award
Summer 2018
Document Type
Thesis
First Advisor
Ahamed, Sheikh Iqbal
Second Advisor
Franco, Zeno
Third Advisor
Kaczmarek, Thomas
Abstract
Risky behavior including violence and aggression, self-injury, anger outburst, domestic violence along with self-injury, sexual abuse, rule breaking, use of drugs and alcohol, suicide etc. are alarming issues among US military veterans who return from combat zone deployment in Iraq and Afghanistan. Veterans are exposed to trauma in war zones which affect most of them with posttraumatic stress disorder (PTSD) or other metal health problems to some degree. Studies have shown that veterans have much higher rates of PTSD than civilians and are more likely to engage in risky behavior. One of the form of displaying and engaging in risky behaviors is through gestures. We collaborated with veterans and social scientists to find the list of 13 gestures that are often used by veterans engaged in risky behaviors. In this research work, we have collect accelerometer data from subjects performing the above-mentioned gestures and tried to detect them using machine learning techniques. The thesis describes identifying gesture clusters from the accelerometer coordinate data and development of a predictive model that is able to classify the gestures resulting towards the prediction of risky behaviors among the veterans who suffer from PTSD.