2024, Vol. 28 ›› Issue (36): 5805-5810

Sensitivity factor analysis of asymmetric gait quality evaluation model based on random forest algorithm

Jiang Meijiao, Zhang Junxia, Shao Yangyang, Lu Fangfang, Yin Guofu, Yang Fang   

Abstract: BACKGROUND: The assessment of asymmetric gait quality plays a pivotal role in guiding rehabilitation training; however, the link between gait quality and kinematic-kinetic gait parameters remains ambiguous.
OBJECTIVE: To formulate a machine-learning model for evaluating gait quality based on gait parameters, identify factors sensitive to gait quality from asymmetric gait parameters, investigate the relationship between gait indicators and gait quality, and provide guidance for asymmetric gait training and rehabilitation.
METHODS: An asymmetric gait database was established through the creation of asymmetric conditions. Kinematic and kinetic data were collected from 8 young and 8 elderly subjects (all male, right dominant population) during gait tests. Gait quality for each test data set was assessed using symmetry indices, resulting in the creation of a gait parameter-gait quality dataset. Utilizing the Random Forest algorithm, a gait quality evaluation model was developed and key quality parameter factors were identified through differential analysis. This model was iteratively refined. The model’s performance was evaluated through 10-fold cross-validation, and its effectiveness was verified using the cross-validation dataset.
RESULTS AND CONCLUSION: (1) A gradient test was designed to categorize gait quality into optimal, suboptimal, intermediate, and poor groups, with 759, 329, 133, and 125 instances, respectively. (2) The application of the Random Forest algorithm in gait quality assessment was explored. A relationship model was established between gait indicators and gait quality, yielding a predictive model accuracy of 95.99%. (3) The 13 main parameters significantly influencing asymmetric gait quality were identified through the Random Forest model’s feature importance ranking. (4) An analysis of gait quality sensitivity factors using the 13 important parameters led to the identification of five key sensitivity indexes. The Random Forest model utilizing these sensitivity factors achieved a predictive accuracy of 94.20%.

Key words: asymmetric gait, gait quality, gait parameter, random forest, feature importance, sensitivity factor, gait quality evaluation