Towards human performance on automatic motion tracking of infant spontaneous movements

Assessment of spontaneous movements can predict the long-term developmental outcomes in high-risk infants. In order to develop algorithms for automated prediction of later function based on early motor repertoire, high-precision tracking of segments and joints are required. Four types of convolutional neural networks were investigated on a novel infant pose dataset, covering the large variation in 1 424 videos from a clinical international community. The precision level of the networks was evaluated as the deviation between the estimated keypoint positions and human expert annotations. The computational efficiency was also assessed to determine the feasibility of the neural networks in clinical practice. The study shows that the precision of the best performing infant motion tracker is similar to the inter-rater error of human experts, while still operating efficiently. In conclusion, the proposed tracking of infant movements can pave the way for early detection of motor disorders in children with perinatal brain injuries by quantifying infant movements from video recordings with human precision.

EfficientPose: Scalable single-person pose estimation

Human pose estimation facilitates markerless movement analysis in sports, as well as in clinical applications. Still, state-of-the-art models for human pose estimation generally do not meet the requirements for real-life deployment. The main reason for this is that the more the field progresses, the more expensive the approaches become, with high computational demands. To cope with the challenges caused by this trend, we propose a convolutional neural network architecture that benefits from the recently proposed EfficientNets to deliver scalable single-person pose estimation. To this end, we introduce EfficientPose, which is a family of models harnessing an effective multi-scale feature extractor, computation efficient detection blocks utilizing mobile inverted bottleneck convolutions, and upscaling improving precision of pose configurations. EfficientPose enables real-world deployment on edge devices through 500K parameter model consuming less than one GFLOP. The results from our experiments, using the challenging MPII single-person benchmark, show that the proposed EfficientPose models substantially outperform the widely-used OpenPose model in terms of accuracy, while being at the same time up to 15 times smaller and 20 times more computationally efficient than its counterpart.