One-year persistence of individual gait patterns identified in a follow-up study – A call for individualised diagnose and therapy

• Published in: Gait & posture Vol. 58; no. NA; pp. 476 - 480
• Main Authors: Horst, F., Mildner, M., Schöllhorn, W.I.
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 01.10.2017
• Subjects: Adult; Biological Variation, Population - physiology; Biomechanical Phenomena; Female; Follow-Up Studies; Gait; Gait - physiology; Healthy Volunteers; Humans; Individual gait patterns; Inter-subject variability; Male; Support vector machines; Uniqueness; Support vector machines; Gait; Individual gait patterns; Inter-subject variability; Uniqueness
• DOI: 10.1016/j.gaitpost.2017.09.003
• ISSN: 0966-6362; 1879-2219

•Unique individual gait patterns identified within a sample of 128 participants.•Individuality of gait patterns is similar to other biometrics like fingerprints.•Individual gait characteristics were persistent over a period of months/years.•Inter-Individual differences may explain most of the variability in gait data.•Advantages for individualised diagnoses and therapy. Although a hunch about the individuality of human movements generally exists, differences in gait patterns between individuals are often neglected. To date, only a few studies distinguished individual gait patterns in terms of uniqueness and emphasised the relevance of individualised diagnoses and therapy. However, small sample sizes have been a limitation on identifying subjects based on gait patterns, and little is known about the permanence of subject-specific characteristics over time. The purpose of this study was (1) to prove the uniqueness of individual gait patterns within a larger sample and (2) to prove the long-term permanence of individual gait patterns. A sample of 128 healthy participants each walked a distance of 10m barefoot 10 times. Two force plates recorded the ground reaction forces during a double step at a self-selected walking speed. A subsample of 46 participants repeated this procedure after a period of 7–16 months. The application of support vector machines resulted in classification rates of 99.8% (1278 out of 1280) and 99.4% (914 out of 920) for the initial subject-classification and the subsample follow-up-classification, respectively. The results showed that gait patterns based on time-continuous ground reaction forces were unique to an individual and could be differentiated from those of other individuals. Support vector machines classified gait patterns to the corresponding individual almost error-free. Hence, human gait is not only different between individuals but also exhibits unique individual characteristics that are persistent over years. Our findings provide evidence for the individual nature of human walking and emphasise the need to evaluate individualised clinical approaches for diagnoses and therapy.