Identifying Asymmetrical Gait Using Neural Network with Marker Coordinates Around the Pelvis and Implication of AI Application in Motion Analysis

Background: Asymmetrical gait refers to an unequal weight-bearing and movement pattern between the two sides of the lower limbs, commonly seen in patients with stroke or cerebral palsy. There is little research exploring how to identify asymmetrical gait using neural network (NN) models. This study aimed to investigate whether NN models would detect asymmetrical gait using marker coordinates around the pelvis. Methods: Twenty healthy volunteers aged between 23 and 35 participated in the study and performed a simulated hemiplegic gait with one leg supported and the other leg straight and swinging laterally; they also walked as normal as a control gait. The four markers around the pelvis were collected via a motion capture system. The movements in the centre of the pelvis (CPL) were calculated as the ranges of motion in 3D space. An NN model was constructed to identify gait modes using CPL moving ranges. Results: The NN model identified gait modes quickly and efficiently with correction rates of approximately 99.56% and 97.61% for asymmetrical and normal gait modes, respectively. The area under the Relative Operating Characteristic curves was approximately 0.992. The movements in the sagittal plane were the most important features contributing to NN modelling. The ratio of the CPL’s moving area in the transverse plane to the product of stride length and step width was higher in the asymmetrical gait than in normal gait. Conclusions: This study showed that the NN models can identify gait modes using a few of the markers in the pelvis. The study highlights NN application in gait recognition and indicates that the NN models could be applied in motion analysis, especially gait analysis for people with asymmetrical conditions.