PERFORMANCE EVALUATION OF ON-DEVICE AI FRAMEWORKS FOR REAL-TIME GESTURE RECOGNITION: A CASE STUDY IN MOBILE ROBOT CONTROL
Description
Thesis focuses on comparing different AI frameworks for real-time gesture recognition on Android devices. I developed an Android application that recognizes human body gestures using AI/Yolo26 and uses them to control a robot via Bluetooth. The study compares TensorFlow Lite, ONNX Runtime, PyTorch Mobile, and NCNN, focusing on processing speed, CPU and memory usage, and overall response time to determine which framework performs best on mobile devices.