Train Activity Recognition Models Using Spectrograms and Computer Vision

Every summer, North Carolina State University's College of Engineering hosts week-long camps for high school students to experience engineering, particularly focusing on computer vision and machine learning. Under the guidance of Dr. Edgar Lobaton, Ph.D. candidates Mohamed Traore, Jeremy Park, and Sanjana Banerjee developed activities that walked students through the computer vision pipeline using Roboflow, from data collection to model prediction. The students used Bluetooth sensors to collect acceleration data during standing, walking, and running, which was then converted into spectrograms to serve as training data for machine learning models. Participants explored Roboflow Universe, learned about image classification, and trained their own activity recognition models with high performance. The initiative aimed to inspire students to create future computer vision applications, with one student expressing interest in developing a skin disease detection tool. The program was supported by NSF grants and emphasized the potential of computer vision in solving global challenges.