ThermoCycleNet: Stereo-based Thermogram Labeling for Model Transition to Cycling
This incremental work addresses the problem of adapting sports medicine thermography models for cycling, benefiting researchers and practitioners in exercise analysis.
The researchers tackled the problem of adapting infrared thermography segmentation models from treadmill running to ergometer cycling by transferring a stereo-based labeling approach and fine-tuning with manual annotations. They found that fine-tuning with a small fraction of manual data improved performance, and combining automatic and manual labels accelerated adaptation to new use cases.
Infrared thermography is emerging as a powerful tool in sports medicine, allowing assessment of thermal radiation during exercise and analysis of anatomical regions of interest, such as the well-exposed calves. Building on our previous advanced automatic annotation method, we aimed to transfer the stereo- and multimodal-based labeling approach from treadmill running to ergometer cycling. Therefore, the training of the semantic segmentation network with automatic labels and fine-tuning on high-quality manually annotated images has been examined and compared in different data set combinations. The results indicate that fine-tuning with a small fraction of manual data is sufficient to improve the overall performance of the deep neural network. Finally, combining automatically generated labels with small manually annotated data sets accelerates the adaptation of deep neural networks to new use cases, such as the transition from treadmill to bicycle.