About this Research Topic
Several internal and external factors have been identified to estimate and control the psycho-biological stress of training in order to optimize training responses and to avoid fatigue, overtraining and other undesirable health effects of an athlete.
An increasing number of lightweight sensor-based wearable technologies (“wearables”) have entered the sports technology market. Non-invasive sensor-based wearable technologies could transmit physical, physiological and biological data to computing platform and may provide through human-machine interaction (smart watch, smartphone, tablet) bio-feedback of various parameters for training load management and health.
However, in theory, several wearable technologies may assist to control training load but the assessment of accuracy, reliability, validity, usability and practical relevance of new upcoming technologies for the management of training load is paramount for optimal adaptation and health.
The aim of this research topic is to
1) identify and critically validate promising wearable technologies to control training load and health in the athletic population,
2) recognize novel data analysis using biomechanical modeling, advanced time series analysis, machine learning, data mining, etc.
3) to aggregate (best-practice) models for controlling load management and health in athletes,
4) stimulate directions for future development in the area of non-invasive sensor-based technologies for load management and health.
An increasing number of lightweight sensor-based wearable technologies (“wearables”) have entered the sports technology market. Non-invasive sensor-based wearable technologies could transmit physical, physiological and biological data to computing platform and may provide through human-machine interaction (smart watch, smartphone, tablet) bio-feedback of various parameters for training load management and health.
However, in theory, several wearable technologies may assist to control training load but the assessment of accuracy, reliability, validity, usability and practical relevance of new upcoming technologies for the management of training load is paramount for optimal adaptation and health.
The aim of this research topic is to
1) identify and critically validate promising wearable technologies to control training load and health in the athletic population,
2) recognize novel data analysis using biomechanical modeling, advanced time series analysis, machine learning, data mining, etc.
3) to aggregate (best-practice) models for controlling load management and health in athletes,
4) stimulate directions for future development in the area of non-invasive sensor-based technologies for load management and health.
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