Arousal pattern analysis of an Olympic champion in ski jumping

Abstract

Mental strength is essential to success in many sports disciplines, especially in professional ski jumping. While physiological signals can reveal information on the mental state, their measurement and analysis for elite ski jumping athletes during competition has not been realised. For the first time in professional ski jumping, we investigated heart rate (HR), its temporal pattern, and corresponding body motion in relation to arousal of the Olympic ski jumping gold medallist Simon Ammann during actual competitions, including his Vancouver 2010 Winter Olympics victory. Using a miniature, on-body ECG monitor with integrated acceleration sensor, we collected a dataset of 99 hours length, including 37 hill jumps. Arousal was assessed from HR data conditioned on body position and acceleration data. The HR and its pattern were analysed during competition days, actual jump situations (training, qualification, and competition) and pre-performance routines. HR was related to the competitiveness of the jump situation, even when physical sports performance remained unchanged. Arousal during jumping and pre-performance routines showed highly reproducible HR patterns. The HR pattern, as assessed by dynamic time warping, deviated during the final Olympic jump, at which time the athlete reported difficulties in regulating arousal in his trained manner. Our approach can be used to collect, analyse, and visualise data to assess an athlete's levels and patterns of arousal during typical competitive situations. We believe that data collected in field-based studies with on-body sensing technology could assist in the design of arousal assessment tools and help facilitate top performance levels.

Estimation of Joint Forces and Moments for the In-Run and Take-Off in Ski Jumping Based on Measurements with Wearable Inertial Sensors

“…Chardonnens et al [ 13 ] also designed and assessed a method to determine some aspects of dynamics using IMUs; the position and velocity of the center of mass perpendicular to the table, the force acting on the center of mass perpendicular to the table, somersault angular velocity during TO and the total aerodynamic force during stable flight. Using a miniature, on-body ECG monitor with an integrated acceleration sensor, Kusserow et al [ 14 ] assessed the arousal pattern from heart rate data. Based on other studies it might be possible and beneficial to design a simple body-worn IMU-based system that would support the calculation of joint dynamics parameters during IR and TO, as well as GRF during TO, which has never been done for a real ski jump.…”

Modeling arousal phases in daily living using wearable sensors

“…Besides sensing modalities that were used to record physiological signals, e.g., electrodermal activity (EDA) [17], the electrocardiogram (ECG) has been shown to provide robust arousal information. The relation to arousal was maintained even when recorded in everyday life [9] and during sports competition situations [18]. Frequently, investigations made use of heart rate (HR) as one feature of the ECG, which can be extracted with a high signal-tonoise ratio (SNR).…”

“…The estimation in (18) is an underestimate of possible differential primitive distribution patterns, because the value for each primitive can vary continuously between À100 and 100 percent resulting in many more combinations. According to (18), there are 50 possible combinations for the configuration of primitives used in this work (two primitive domains chest and thigh with three classes each, and the primitive L2.Var:…”

“…On the other hand, a too high number of clusters can result in overfitting, where each activation is assigned to one cluster. Theoretically, the number of combinations of differential primitive distribution patternsĈ ðqÞ can be estimated using (18), with q denoting the number of primitive domains, and N i the number of primitives in the respective primitive domain î…”

Repeated stage exposure reduces music performance anxiety

“…Thus, an important marker of increased sympathetic activity, and therefore of MPA, is a higher HR ( Schmidt et al, 2019 ). Not surprisingly, several authors have included HR measurements in studies on for example stress during sports ( Oudejans and Pijpers, 2009 , 2010 ; Kusserow et al, 2010 ), repeated psychosocial stress induced by real and virtual stressors, including the same stressor over different periods of time ( Schommer et al, 2003 ; Kothgassner et al, 2021 , 2022 ), acute psychological stress ( Trotman et al, 2019 ) and MPA ( Yoshie et al, 2008 ; Yoshie et al, 2009 ; Kusserow et al, 2012 ; Kenny et al, 2013 ; Studer et al, 2014 ). In addition to HR, several authors have used heart rate variability (HRV) in the context of sports and exercise psychology to measure stress ( Mosley and Laborde, 2022 ).…”