Knee injuries are frequent, varied and often require the patient to undergo intensive rehabilitation for several months. Treatment protocols usually contemplate some recurrent measurements in order to assess progress, such as goniometry. The need for specific equipment or the complexity and duration of these tasks cause them to often be neglected. A novel deep learning based solution is presented, supported by the generation of a synthetic image dataset. A 3D human-body model was used for this purpose, simulating a recovering patient. For each image, the coordinates of three key points were registered: the centers of the thigh, the knee and the lower leg. These values are sufficient to estimate the flexion angle. Convolutional neural networks were then trained for predicting these six coordinates. Transfer learning was used with the VGG16 and InceptionV3 models pre-trained on the ImageNet dataset, being an additional custom model trained from scratch. All models were tested with different combinations of data augmentation techniques applied on the training sets. InceptionV3 achieved the best overall results, producing considerably good predictions even on real unedited pictures.
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