Multi-modal Deep Neural Features for Classification of Gait Abnormality

Clinical gait analysis plays a vital role in diagnosis and monitoring neurological and musculoskeletal injuries. Quali-tative gait assessment depends on subjective observations, manual measurements, and specialized equipment. Recently machine learning and deep learning based models have demonstrated sig-nificant accuracy in gait analysis. But dynamic feature extraction is always a challenging problem in temporal gait data analysis. After extracting dynamic features, a Fully-connected Neural Network (FNN) is employed to classify of gait abnormalities using GaitRec standard dataset. The proposed multi-modal features based classification model achieves 96.22 % accuracy and it outperforms state-of-the-art methods.