Gait Analysis through Animated GIFs: A Novel Approach for Visualizing Step Dynamics - Sharma, Roopali S; Taterh, Dr. Swapnesh; S, Deepali (South Eastern European Journal of Public Health, 2024)
The field of prosthetic knee development has made great progress in recent years, notably with the use of machine learning (ML) approaches. Researchers made significant progress in enhancing the functionality, flexibility, and user experience of prosthetic knees using revolutionary machine learning techniques. Since gait analysis delivers part of the most important information about human motion patterns, it becomes critical in the development of a prosthetic knee. In this paper, some new techniques for gait analysis with feature extraction are proposed that include visualization using animated GIF images. This methodology extracts key parameters like step length and step width from the frames of a GIF image and can completely represent the dynamics of gait with respect to time. This methodology is particularly significant to design an efficient prosthetic knee since it shows how combining computer analysis and visual data may enhance gait analysis methods. For this study, brief walking footage of both healthy individuals and amputees are recorded, then turned into GIFs. Subsequently, these GIF data points are retrieved, making it possible to quantify step length and other essential metrics required to analyses gait patterns. Gait analysis is incorporated as part of the development process for these prosthetic knee joints due to the fact that it gives information on human motion patterns. The methods used in this paper provide novel ways of carrying out gait analysis through feature extraction and visualization using animated GIF images. This method would help in depicting the full picture of gait dynamics with time and assist in extracting important parameters such as step length and width from the frames of the GIF. The work, as described, falls squarely into the use of computer vision techniques for feature extraction and into time-series data analysis for trend identification within the broader application of machine learning in the development of the prosthetic knee. These techniques could be generalized toward designing better prosthetic knee design for more natural and fluent motion.