Adaptive clustering of IMU-based gait models for personalized clinical gait analysis

Real-time home healthcare, enabled by future sixth-generation (6G) communication technology, offers a promising application for advanced clinical gait analysis. Utilization of dynamic sensors in clinical gait analysis presents a definitive approach for quantifying variances in gait patterns between patient cohorts and healthy populations. However, variations in demographic and anthropometric parameters, including gender, age, height, and Body Mass Index, among these groups, may result in inaccurate clinical evaluations. This study introduces an innovative clustering methodology that incorporates custom-weighted distances based on demographic and anthropometric parameters. This approach is designed to establish a dynamic, normative gait framework pertinent to healthy individuals. It facilitates the classification of individuals into distinct categories grounded on their demographic and anthropometric parameters, enabling the provision of a customized normative gait model appropriate for each category.