Motor delay image recognition based on deep learning and human skeleton model

Children with developmental delays often exhibit gross motor delay during their early childhood. Identifying such delay typically requires standardized assessments, but these assessments are susceptible to subjective interpretation. To enhance objectivity and reliability in these assessments, this study proposes a novel method utilizing an AI model for identifying gross motor delay in children. The proposed method divides the assessment of gross motor skills into two components: static balance and dynamic balance. First, the Yolo target detection model and the Mediapipe human posture detection model are employed for preprocessing to determine skeletal joints and body positioning information. Subsequently, dimensionality reduction techniques are applied to extract key features. A sliding window method is then used to extract action features identifying static balance features of the human body (e.g., standing on one foot, standing on the left foot) and dynamic balance features (e.g., lifting the heels while walking, jumping on a mat). The machine learning classifier and LSTM-CNN are then used to categorize the static and dynamic balance feature data. Experimental results demonstrate that the proposed method achieved accuracy scores of 80 % and 87 % for static and dynamic balancing skills, respectively.