2020
DOI: 10.1007/s12283-020-00328-9
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Analysis of physiological changes related to emotions during a zipline activity

Abstract: Despite the popularity of physiological wearable sensors in sport activities to provide feedback on athletes' performance, understanding the factors influencing changes in athletes' physiological rhythms remains a challenge. Changes in physiological rhythms such as heart rate, breathing rate or galvanic skin response can be due to both physical exertion and psychoemotional states. Separating the influence of physical exertion and psychoemotional states in activities that involves both is complicated. As a resu… Show more

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Cited by 5 publications
(4 citation statements)
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“…It is a statistical technique that models non-linear relationships between time-varying predictors and outcome variables. It has been used in domains such as acoustic and physiological analyses [14,29,60], as it combines the strengths of flexibility (as in machine-learning techniques) and interpretability (defined as an addition of linear models). GAMMs facilitate deriving a model for a cluster of time series data by applying non-parametric smoothing functions and comparing significant differences between multiple models; therefore, suitable for testing our hypotheses.…”
Section: Analyses and Resultsmentioning
confidence: 99%
“…It is a statistical technique that models non-linear relationships between time-varying predictors and outcome variables. It has been used in domains such as acoustic and physiological analyses [14,29,60], as it combines the strengths of flexibility (as in machine-learning techniques) and interpretability (defined as an addition of linear models). GAMMs facilitate deriving a model for a cluster of time series data by applying non-parametric smoothing functions and comparing significant differences between multiple models; therefore, suitable for testing our hypotheses.…”
Section: Analyses and Resultsmentioning
confidence: 99%
“…This limitation has been pointed by other researchers [33]. Some of them have shown that the combination of sensors signals, for example in the General Additive Mixed Model (GAMM), can provide more information of the tendency of physiological reactions [34] [33]. In Figure 4 we see an example of one GAMMs model (Fig.…”
Section: Perspectives and Conclusionmentioning
confidence: 94%
“…Clear methods for the treatment and statistical analysis of data should be established in future research. This limitation has been pointed by other researchers [33]. Some of them have shown that the combination of sensors signals, for example in the General Additive Mixed Model (GAMM), can provide more information of the tendency of physiological reactions [34] [33].…”
Section: Perspectives and Conclusionmentioning
confidence: 99%
“…Bi et al [ 19 ] also used commercial wearables but to recognise the emotional state of long-distance runners during a race in order to create affective maps that are shared with the spectators. The works of Dupre are focused on the use of wearables for modelling emotional patterns in athletes during physical activities [ 20 ] and for analysing the relationship between their emotions and their performance when performing zipline activities [ 21 ]. There are also many works that use wearables and consider athletes’ emotions to detect stress [ 22 ], pain [ 23 ] or fatigue perception [ 4 , 24 ].…”
Section: Introductionmentioning
confidence: 99%