MS in Computer Engineering (M.S.C.P.)
Degree Granting Department
Shaun Canavan, Ph.D.
Robert Karam, Ph.D.
Tempestt Neal, Ph.D.
Emotion Recognition, Jetson Nano, Machine Learning, ONNX, Raspberry Pi
Emotion recognition is a quickly growing field of study due to the increased interest in building systems which can classify and respond to emotions. Recent medical crises, such as the opioid overdose epidemic in the United States and the global COVID-19 pandemic has emphasized the importance of emotion recognition applications is areas like Telehealth services. Considering this, this thesis focuses specifically on pain recognition. The problem of pain recognition is approached from both a hardware and software perspective, as we propose a real-time pain recognition system, from facial images, that is deployed on an NVIDIA Jetson Nano single-board computer. We have conducted offline experiments using the BP4D dataset, where we investigate the impact of gender and data imbalance. This thesis proposes an affordable and easily accessible system which could perform pain recognition inferences. The results from this study found a balanced dataset, in terms of class and gender, results in the highest accuracies for pain recognition. We also detail the difficulties of pain recognition using facial images and propose some future work that can be investigated for this challenging problem.
Scholar Commons Citation
Tynes, Iyonna L., "Pain Recognition Performance on a Single Board Computer" (2021). USF Tampa Graduate Theses and Dissertations.