2017
DOI: 10.6018/analesps.33.3.271011
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¿Cómo combinar datos observacionales y fisiológicos? Un estudio de caso de habilidades motrices y frecuencia cardíaca en programas de actividad física para mujeres

Abstract: Título: ¿Cómo combinar datos observacionales y fisiológicos? Un estudio de caso de patrones de habilidades motoras y frecuencia cardíaca en programas de ejercicio para mujeres adultas. Resumen: El presente estudio analiza las respuestas individuales y de grupo de la frecuencia cardiaca de mujeres adultas mientras realizan actividad física. El interés de este estudio recae en comparar como estas respuestas varían entre sesiones de ejercicio rutinario y sesiones que incluyen variedad de habilidades motrices. Par… Show more

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Cited by 8 publications
(3 citation statements)
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References 39 publications
(27 reference statements)
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“…Examples are (a) integration of data through merging, connecting, and embedding strategies (Plano Clark and Sanders, 2015); (b) integration of multisensor data through data fusion (Liggins et al, 2017), which consists of combining signal- and image-processing techniques with pattern-recognition techniques and artificial intelligence to create multimodal databases; (c) integration of heart rate data captured during exercise with observational data on physical activity through hidden Markov chains (Castañer et al, 2017b); and (d) application of deep learning techniques, which automatically extract multilevel characteristics that maximize the identification of predefined behavioral patterns (Ordóñez and Roggen, 2016). The resulting information is also richer in terms of veracity, as the data are not tainted by a personal opinion but based on an objective recording of what happened.…”
Section: Inclusion Of Purely Observational Sports and Physical Activimentioning
confidence: 99%
“…Examples are (a) integration of data through merging, connecting, and embedding strategies (Plano Clark and Sanders, 2015); (b) integration of multisensor data through data fusion (Liggins et al, 2017), which consists of combining signal- and image-processing techniques with pattern-recognition techniques and artificial intelligence to create multimodal databases; (c) integration of heart rate data captured during exercise with observational data on physical activity through hidden Markov chains (Castañer et al, 2017b); and (d) application of deep learning techniques, which automatically extract multilevel characteristics that maximize the identification of predefined behavioral patterns (Ordóñez and Roggen, 2016). The resulting information is also richer in terms of veracity, as the data are not tainted by a personal opinion but based on an objective recording of what happened.…”
Section: Inclusion Of Purely Observational Sports and Physical Activimentioning
confidence: 99%
“…Furthermore, mixed laterality can be a versatile and rich skill to perform successfully in multifaceted environments (Chapple and Johnson, 2007), and of interest to optimize the athletes' complex movements performance (Loffing et al, 2016). In this regard, previous research in football concluded that Lionel Messi—a left-footed top-player—is a good example of motor versatility as he uses successfully mixed laterality in several motor skills (Castañer et al, 2016, 2017a,b).…”
Section: Discussionmentioning
confidence: 99%
“…This approach to methodological integration has been applied in psychological research on spontaneous behavior in the fields of health (e.g., Casarrubea et al, 2018;Castañer et al, 2017;Puigarnau et al, 2016), physical activity and sport (e.g., Amatria et al, 2017;Fernandes et al, 2020;Fernández-Hermógenes et al, 2021;Gutiérrez-Santiago, 2013;Lapresa et al, 2018;Prieto-Lage et al, 2020;Sastre et al, 2021), and physical education (e.g., Camerino et al, 2019;Castañer et al, 2009Castañer et al, , 2011Castañer et al, , 2020Prat et al, 2019;Valero-Valenzuela et al, 2020). Indeed, there are several software programs to conduct visualization and annotation, but with some weakness related to all the necessary features of viewing, coding, analysis and administration of the results, resulting in scattered data and necessitating the export of these results to other complementary software programs (Hernández-Mendo et al, 2014;Love et al, 2019).…”
Section: Introductionmentioning
confidence: 99%