Measurement method of determining natural and unnatural gaits using autocorrelation coefficients

Walking is the most common physical activity in humans, and gait can be used as a measure of human health. If the gait is unnatural or uncomfortable, it indicates a problem inside or outside the person’s body. In particular, for the elderly, walking is used as an important indicator of their health status. In this study, we developed an algorithm that can determine whether human walking is natural or unnatural, by comparing the autocorrelation coefficients of the left and right foot. We used F1-scores to measure the accuracy of the gait result determined by the algorithm. Natural walking was accurately distinguished with 80% accuracy, and unnatural with 60% accuracy. Owing to the splint attached to one foot to express unnatural walking, both feet affected gait, resulting in slightly lower accuracy than natural walking. As a future study, it is possible to devise a method to improve accuracy by extracting various gait features that can be obtained through gait and using artificial intelligence algorithms such as machine learning or deep learning.