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A-GAS: a Probabilistic Approach for Generating Automated Gait Assessment Score for Cerebral Palsy Children
  • Rishabh Bajpai ,
  • Deepak Joshi
Rishabh Bajpai
Indian Institute of Technology Delhi, Indian Institute of Technology Delhi

Corresponding Author:[email protected]

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Deepak Joshi
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Abstract

Gait disorders in children with cerebral palsy (CP) affect their mental, physical, economic, and social lives. Gait assessment is one of the essential steps of gait management. It has been widely used for clinical decision making and evaluation of different treatment outcomes. However, most of the present methods of gait assessment are subjective, less sensitive to small pathological changes, time-taking and need a great effort of an expert. This study proposes an automated, comprehensive gait assessment score (A-GAS) for gait disorders in CP. Kinematic data of 356 CP and 41 typically developing subjects is used to validate the performance of A-GAS. For the computation of A-GAS, instance abnormality index (AII) and abnormality index (AI) are computed. AII quantifies gait abnormality of a gait cycle instance, while AI quantifies gait abnormality of a joint angle profile. AII is calculated for all gait cycle instances by performing probabilistically and statistical tests. Abnormality index (AI) is a weighted sum of AII, computed for each joint angle profile. A-GAS is a weighted sum of AI, calculated for a lower limb. Moreover, a graphical representation of the gait assessment report, including AII, AI, and A-GAS is generated to understand the results better. Furthermore, the study compares A-GAS with a present rating-based gait assessment scores to understand fundamental differences between them. Finally, AGAS’s performance is verified for a high-cost multicamera set-up using nine joint angle profiles and a low-cost single camera set-up using three joint angle profiles. Results show no significant differences in performance of A-GAS for both the set-ups. Therefore, A-GAS for both the set-ups can be used interchangeably.
2021Published in IEEE Transactions on Neural Systems and Rehabilitation Engineering volume 29 on pages 2530-2539. 10.1109/TNSRE.2021.3131466