A Decade of Progress in Video-Based Infant Movement Assessment: A Comprehensive Survey of Methods, Applications, and Datasets

Video-based analysis of infant movement has emerged as a promising approach for the early detection of neurodevelopmental disorders. The field has evolved from subjective clinical assessment to integrated, quantitative, and automated systems. This comprehensive survey charts the technological evolution of the field from 2014 to 2024, focusing on three core areas: pose estimation methods adapted for infant subjects, direct video analysis approaches, and dataset collection methodologies. The survey systematically analyzes the progression from basic motion tracking to advanced deep learning solutions, with particular attention to key developmental periods, including writhing movements, fidgety movements, and voluntary movements. The review highlights both significant advances and persistent challenges. Successful adaptations of pose estimation techniques to infant characteristics contrast with unresolved issues in data acquisition, privacy preservation, and clinical integration. A growing emphasis on explainable artificial intelligence is essential for fostering clinical trust and adoption. A comparative analysis of datasets and monitoring systems clarifies their respective strengths and limitations. Key challenges include the scarcity of large and diverse datasets, the lack of standardization across platforms, and the need for robust clinical validation studies. The findings suggest that while technical capabilities have advanced considerably, successful clinical implementation requires careful consideration of practical constraints and ethical issues. This survey serves as a comprehensive reference for researchers and clinicians working at the intersection of computer vision, machine learning, and pediatric healthcare.