Evaluating the Effectiveness of Pre-trained Neural Networks in Automatic Sit-up Counter for Civil Servant Physical Test

In the domain of machine learning and computer vision, employing deep learning models for real-time object detection tasks, especially in low-resource environments, poses significant challenges. This study aimed to assess the effectiveness and efficiency of five pre-trained neural network architectures—SqueezeNet, MobileNetV3, ResNet50, VGG16, and InceptionNet—for detecting and counting sit-ups from video footage. The evaluation criteria included accuracy in sit-up counting compared to manual methods and the computational resources utilized. Among the models examined, SqueezeNet demonstrated notable performance, closely aligning with manual counts and outperforming larger, presumably more complex networks. Despite its compact size of 3.33 MB, SqueezeNet showcased the potential for achieving high detection accuracy without the need for extensive network complexity. Conversely, larger networks such as VGG16, MobileNetV3, ResNet50, and InceptionNet exhibited significant discrepancies in sit-up counts, indicating potential overfitting during training and a lack of generalizability to real-world data. Regarding computational resources, all networks displayed minimal memory usage and CPU utilization, highlighting the feasibility of deploying them on low-resource platforms like Raspberry Pi. This study underscores the promise of lightweight deep learning models like SqueezeNet for real-time object detection tasks and provides valuable insights for future research and applications in health and fitness monitoring.