Deep Learning in Left and Right Footprint Image Detection Based on Plantar Pressure

Featured Application: In this study, the left and right footprint images were predicted based on deep learning object detection models. YOLOv4 models are the most balanced deep learning models in detecting left and right feet from footprint images. In different object detection models, the right foot showed higher accuracy than the left foot. People with cerebral palsy (CP) suffer primarily from lower-limb impairments. These impairments contribute to the abnormal performance of functional activities and ambulation. Footprints, such as plantar pressure images, are usually used to assess functional performance in people with spastic CP. Detecting left and right feet based on footprints in people with CP is a challenge due to abnormal foot progression angle and abnormal footprint patterns. Identifying left and right foot profiles in people with CP is essential to provide information on the foot orthosis, walking problems, index gait patterns, and determination of the dominant limb. Deep learning with object detection can localize and classify the object more precisely on the abnormal foot progression angle and complex footprints associated with spastic CP. This study proposes a new object detection model to auto-determine left and right footprints. The footprint images successfully represented the left and right feet with high accuracy in object detection. YOLOv4 more successfully detected the left and right feet using footprint images compared to other object detection models. YOLOv4 reached over 99.00% in various metric performances. Furthermore, detection of the right foot (majority of people’s dominant leg) was more accurate than that of the left foot (majority of people’s non-dominant leg) in different object detection models.