Cross-Subject and Cross-Modal Transfer for Generalized Abnormal Gait Pattern Recognition

• Published in: IEEE transaction on neural networks and learning systems Vol. 32; no. 2; pp. 546 - 560
• Main Authors: Gu, Xiao, Guo, Yao, Deligianni, Fani, Lo, Benny, Yang, Guang-Zhong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abnormalities; Algorithms; Biomechanical Phenomena; Biometry; Body sensor network; Computer Systems; Data mining; Deep Learning; Electromyography; Feature extraction; Feature recognition; Gait; gait analysis; Gait Disorders, Neurologic - diagnosis; Gait recognition; Health care; Home Environment; Humans; Imaging, Three-Dimensional; Joints - diagnostic imaging; Kinematics; Lower Extremity - diagnostic imaging; model generalization; Motion capture; multimodal representation; Neural Networks, Computer; New technology; Noise measurement; Pattern recognition; Pattern Recognition, Automated - methods; Representations; Reproducibility of Results; Sensors; Skeleton; Wearable Electronic Devices; Wearable technology

For abnormal gait recognition, pattern-specific features indicating abnormalities are interleaved with the subject-specific differences representing biometric traits. Deep representations are, therefore, prone to overfitting, and the models derived cannot generalize well to new subjects. Furthermore...