Wearable computing systems and machine learning for sports science researchSpeaker: Bjoern M Eskofier – Erlangen, Germany
Topic(s): Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing
Wearable computing systems play an increasingly important role in recreational and elite sports. They comprise of two parts. First, sensors for physiological (ECG, EMG, ...) and biomechanical (accelerometer, gyroscope, ...) data recording are embedded into clothes and equipment. Second, embedded microprocessors (e.g. in smartphones) are used for monitoring and analysis of the recorded data. Together, these systems can provide real-time information and feedback for scientific studies in real sports situations.
Data mining concepts provide tools for analyzing the considerable amount of physiological and biomechanical data that is generated in sports science studies. Especially when using wearable computing systems, the number of participants and variety of measured data is unlimited in general. Traditional statistical analysis methods commonly cannot handle this amount of data easily. Thus, the analysis is often restricted to individual variables rather than multidimensional dependencies and a considerable amount of information is neglected. Moreover, the results are frequently biased by the expectation of the researcher. Here, the objective, data-driven methods from data mining can contribute by offering useful tools for the analysis tasks. These tools have the ability to deal with large data sets, to analyze multiple dimensions simultaneously, to work data-driven rather than hypothesis-driven, and to provide valuable insights into training effects and injury risks.
About this LectureNumber of Slides: 20 - 40
Duration: 20 - 60 minutes
Languages Available: English, German
Request this Lecture
To request this particular lecture, please complete this online form.
Request a Tour
To request a tour with this speaker, please complete this online form.
All requests will be sent to ACM headquarters for review.