Dataset Information

The Gaitprint: Identifying Individuals by Their Running Style.

ABSTRACT: Recognizing the characteristics of a well-developed running style is a central issue in athletic sub-disciplines. The development of portable micro-electro-mechanical-system (MEMS) sensors within the last decades has made it possible to accurately quantify movements. This paper introduces an analysis method, based on limit-cycle attractors, to identify subjects by their specific running style. The movement data of 30 athletes were collected over 20 min. in three running sessions to create an individual gaitprint. A recognition algorithm was applied to identify each single individual as compared to other participants. The analyses resulted in a detection rate of 99% with a false identification probability of 0.28%, which demonstrates a very sensitive method for the recognition of athletes based solely on their running style. Further, it can be seen that these differentiations can be described as individual modifications of a general running pattern inherent in all participants. These findings open new perspectives for the assessment of running style, motion in general, and a person's identification, in, for example, the growing e-sports movement.

SUBMITTER: Weich C

PROVIDER: S-EPMC7412195 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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The Gaitprint: Identifying Individuals by Their Running Style.

Weich Christian C M Vieten Manfred M

Sensors (Basel, Switzerland) 20200708 14

Recognizing the characteristics of a well-developed running style is a central issue in athletic sub-disciplines. The development of portable micro-electro-mechanical-system (MEMS) sensors within the last decades has made it possible to accurately quantify movements. This paper introduces an analysis method, based on limit-cycle attractors, to identify subjects by their specific running style. The movement data of 30 athletes were collected over 20 min. in three running sessions to create an ind ...[more]

PMID: 32650424

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Project description:Abnormal lower limb biomechanics is speculated to be a risk factor for Achilles tendinopathy. This study systematically reviewed the existing literature to identify, critique and summarise lower limb biomechanical factors associated with Achilles tendinopathy.We searched electronic bibliographic databases (Medline, EMBASE, Current contents, CINAHL and SPORTDiscus) in November 2010. All prospective cohort and case-control studies that evaluated biomechanical factors (temporospatial parameters, lower limb kinematics, dynamic plantar pressures, kinetics [ground reaction forces and joint moments] and muscle activity) associated with mid-portion Achilles tendinopathy were included. Quality of included studies was evaluated using the Quality Index. The magnitude of differences (effect sizes) between cases and controls was calculated using Cohen's d (with 95% CIs).Nine studies were identified; two were prospective and the remaining seven case-control study designs. The quality of 9 identified studies was varied, with Quality Index scores ranging from 4 to 15 out of 17. All studies analysed running biomechanics. Cases displayed increased eversion range of motion of the rearfoot (d = 0.92 and 0.67 in two studies), reduced maximum lower leg abduction (d = -1.16), reduced ankle joint dorsiflexion velocity (d = -0.62) and reduced knee flexion during gait (d = -0.90). Cases also demonstrated a number of differences in dynamic plantar pressures (primarily the distribution of the centre of force), ground reaction forces (large effects for timing variables) and also showed reduced peak tibial external rotation moment (d = -1.29). Cases also displayed differences in the timing and amplitude of a number of lower limb muscles but many differences were equivocal.There are differences in lower limb biomechanics between those with and without Achilles tendinopathy that may have implications for the prevention and management of the condition. However, the findings need to be interpreted with caution due to the limited quality of a number of the included studies. Future well-designed prospective studies are required to confirm these findings.

Dataset Information

The Gaitprint: Identifying Individuals by Their Running Style.

Publications

The Gaitprint: Identifying Individuals by Their Running Style.

Similar Datasets

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