Self-Organizing Maps and Fuzzy C-Means Algorithms on Gait Analysis Based on Inertial Sensors Data

Human gait corresponds to the physiological way of locomotion, which can be affected by several injuries. Thus, gait analysis plays an important role in observing kinematic and kinetic parameters of the joints involved with such movement pattern. Due to the complexity of such analysis, this paper explores the performance of two adaptive methods, Fuzzy c-means (FCM) and Self-organizing maps (SOM), to simplify the interpretation of gait data, provided by a secondary dataset of 90 subjects, subdivided into six groups. Based on inertial measurement units (IMU) data, two kinematic features, average cycle time and cadence, were used as inputs to the adaptive algorithms. Considering the similarities among the subjects of such database, our experiments show that FCM presented a better performance than SOM. Despite the misplacement of subjects into unexpected clusters, this outcome implies that FCM is rather sensitive to slight differences in gait analysis. Nonetheless, further trials with the aforementioned methods are necessary, since more gait parameters and a greater sample could reveal an undercover variation within the proper walking pattern.