Validation of two hybrid approaches for clustering age-related groups based on gait kinematics data

• Published in: Medical engineering & physics Vol. 78; pp. 90 - 97
• Main Authors: Caldas, Rafael, Sarai, Rebeca, Buarque de Lima Neto, Fernando, Markert, Bernd
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.04.2020
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Aging - physiology; Biomechanical Phenomena; Child; Child, Preschool; Cluster Analysis; Feature selection; Female; Fuzzy logic; Gait Analysis; Humans; Inertial measurement unit; Male; Mechanical Phenomena; Middle Aged; Self-organizing maps algorithm; Young Adult; Fuzzy logic; Feature selection; Inertial measurement unit; Self-organizing maps algorithm; Gait analysis
• ISSN: 1350-4533; 1873-4030; 1873-4030
• DOI: 10.1016/j.medengphy.2020.02.001

•The hybrid approaches presented a similar performance, overpowering the FCM algorithm.•The SOM + KM is recommended for demanding less computational resources than the SOM + FCM.•Cadence stands out as the most relevant gait aspect to cluster age-related groups. Age-associated changes in walking parameters are relevant to recognize functional capacity and physical performance. However, the sensible nuances of slightly different gait patterns are hardly noticeable by inexperienced observers. Due to the complexity of this evaluation, we aimed at verifying the efficiency of applied hybrid-adaptive algorithms to cluster groups with similar gait patterns. Based on self-organizing maps (SOM), k-means clustering (KM), and fuzzy c-means (FCM), we compared the hybrid algorithms to a conventional FCM approach to cluster accordingly age-related groups. Additionally, we performed a relevance analysis to identify the principal gait characteristics. Our experiments, based on inertial-sensors data, comprised a sample of 180 healthy subjects, divided into age-related groups. The outcomes suggest that our methods outperformed the FCM algorithm, demonstrating a high accuracy (88%) and consistent sensitivity also to distinguish groups that presented a significant difference (p < .05) only in one of the six observed gait features. The applied algorithms showed a compatible performance, but the SOM + KM required less computation cost and, therefore, was more efficient. Furthermore, the results indicate the overall importance of cadence, as a measurement of physical performance, especially when clustering subjects by their age. Such output provides valuable information to healthcare professionals, concerning the subject’s physical performance related to his age, supporting and guiding the physical evaluation.