Analysis of Fluency of Movement in Parkour Using a Video and Inertial Measurement Unit Technology

Abstract

Fluency is a movement parameter combining smoothness and hesitation, and its objective measurement may be used to determine the effects of practice on sports performance. This study aimed to measure fluency in parkour, an acrobatic discipline comprising complex non-cyclical movements, which involves fluency as a critical aspect of performance. Inter-individual fluidity differences between advanced and novice athletes as well as intra-individual variations of fluency between different parts and subsequent repetitions of a path were addressed. Seventeen parkour participants were enrolled and divided into two groups based on their experience. We analysed signals captured from an inertial measurement unit fixed on the back of the pelvis of each participant during three consecutive repetitions of a specifically designed parkour routine under the guidance of video analysis. Two fluency parameters, namely smoothness and hesitation, were measured. Smoothness was calculated as the number of inflexions on the so-called jerk graph; hesitation was the percentage of the drop in the centre of mass velocity. Smoothness resulted in significantly lower values in advanced athletes (mean: 126.4; range: 36–192) than in beginners (mean: 179.37; range: 98–272) during one of the three motor activities (p = 0.02). A qualitative analysis of hesitation showed that beginner athletes tended to experience more prominent velocity drops and negative deflection than more advanced athletes. In conclusion, a system based on a video and an inertial measurement unit is a promising approach for quantification and the assessment of variability of fluency, and it is potentially beneficial to guide and evaluate the training process.

Telemedicine in Sports under Extreme Conditions: Data Transmission, Remote Medical Consultations, and Diagnostic Imaging

“…In extreme sports, even more than in traditional sports, the noncyclical nature of activities, outdoor settings, and environmental variables hinder data capture, making it challenging to deconstruct the movements in their patterns and collect critical neurophysiological and cinematic data [63].…”

“…Adopting technological advances to reveal athletic gesture characteristics and highlight subtle movement patterns directly in the field can allow adapting the intrinsic dynamics of learners by enhancing their relations with the performance environment and manipulating constraints, leading to individualised movement responses [63].…”