About this Research Topic
The quality and quantity of sports technologies that can be used to maximise performance, minimise the risk of injury, and monitor training are rapidly expanding. Coaches, trainers, clinicians, and athletes benefit from the continuous emergence of feedback tools that help enhance motor skill development and monitor training load. A substantial market exists for the commercialisation of sports technologies that are backed by evidenced-based research and have been tested on the field, in the pool, or on the court. Approaches developed in areas outside sport, like advanced statistical modelling for stock market and weather forecasting and computer vision for facial recognition, have been adopted by exercise and sports science researchers to provide new techniques to answer long-standing questions.
The motto “Beat Moore’s Law” has reduced the size of wearable devices into manageable and realistic units that allow athletes to move more naturally than what was available only half a decade ago. The capability of specialised, multi-camera motion capture is being folded into low-cost, portable systems that can perform pose estimation outside the lab. Smartphone and tablet apps are the new norm to host dashboards for analysing human performance. Human performance data processing is becoming automated and fast, allowing coaches, trainers, and clinicians to provide immediate and meaningful feedback to their athletes. These advancements are simultaneously the product of and driving force behind world-class research that ensures these tools are effectively enhancing sport performance and minimising injury risk through motor behaviour change.
The aim of this Research Topic is to disseminate information about the latest technologies that help to enhance quantitative feedback in sport with the purpose of maximising performance and minimising injury through skill acquisition, technique development, and load monitoring.
Areas of interest include, but are not limited to:
• Out-of-lab and in-field applications of innovative technology
• Real-time monitoring and feedback using technology (e.g., wearable devices, computer vision)
• Statistical modelling and machine learning approaches to monitoring performance and injury that are novel in sport
• Expedition of biomechanical data acquisition
• Development and proven effective use of dashboards for athlete feedback and/or load monitoring
The motto “Beat Moore’s Law” has reduced the size of wearable devices into manageable and realistic units that allow athletes to move more naturally than what was available only half a decade ago. The capability of specialised, multi-camera motion capture is being folded into low-cost, portable systems that can perform pose estimation outside the lab. Smartphone and tablet apps are the new norm to host dashboards for analysing human performance. Human performance data processing is becoming automated and fast, allowing coaches, trainers, and clinicians to provide immediate and meaningful feedback to their athletes. These advancements are simultaneously the product of and driving force behind world-class research that ensures these tools are effectively enhancing sport performance and minimising injury risk through motor behaviour change.
The aim of this Research Topic is to disseminate information about the latest technologies that help to enhance quantitative feedback in sport with the purpose of maximising performance and minimising injury through skill acquisition, technique development, and load monitoring.
Areas of interest include, but are not limited to:
• Out-of-lab and in-field applications of innovative technology
• Real-time monitoring and feedback using technology (e.g., wearable devices, computer vision)
• Statistical modelling and machine learning approaches to monitoring performance and injury that are novel in sport
• Expedition of biomechanical data acquisition
• Development and proven effective use of dashboards for athlete feedback and/or load monitoring
Keywords: Athlete performance, injury monitoring, skill acquisition, wearable devices, machine learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
