Abnormal gait partitioning and real-time recognition of gait phases in children with cerebral palsy

Objective: Since the gait of children with cerebral palsy (CP) lacks distinct foot event characteristics, it's difficult to accurately distinguish the key gait events, especially in the stance phase. To solve the problem of gait phase detection for children with CP, a unique gait classification system is designed to detect the walking maneuvers during a gait cycle. This system can detect all gait phases of CP patients, and send the kinematic information to a rule-based algorithm through a series of sensors installed in the legs to identify the salient features of the signal. Methods: In the light of the quasi-stiffness gait phase transformation as the classification guide, two sets of motion signal feature sets are extracted from the leg feature signals of 10 CP children, and two gait stage recognition algorithms are proposed to divide the six gait sub-phases. Results: The proposed algorithms of peak-zero-based angular velocity and thresholding-based angular realize the effective recognition of gait phases, especially for the stance phase. Conclusion: The proposed methods have the potential to obtain correct decoding of motion information and recognize gait phases. Significance: This study provides a new idea to solve the problem of difficult identification of gait phase for children with CP, which can be applied to a behavior-driven lower limb exoskeleton robot system for assisting walking and gait correction.