AbnormNet: A Neural Network Based Suggestive Tool for Identifying Gait Abnormalities in Cerebral Palsy Children

Abstract

Cerebral palsy (CP) is a neurological disorder that affects movements, coordination and muscle tone. This paper presents a neural network model having three modules for classifying walking patterns into nine known knee joint abnormalities in the sagittal plane. The training of the neural network is divided into three phases. In the first phase, the network is trained to learn the abnormality of each gait cycle instance. In the second phase, the network is trained to identify the relation between the anomaly of gait cycle instance and the nine known abnormal walking patterns. In the third phase, the network is fine-tuned. Further, the performance of the proposed model is compared with two other training conditions namely, ‘only two phases of training are done’ and ‘all modules are trained together’. The network obtained the best classification accuracy of 98%, the precision of 0.93, recall of 0.95 and f1-score of 0.95. These results suggest that a neural network-based method can be used as a gait assessment tool for known gait abnormalities.