Wearable Computing and Machine Learning for Applications in Sports, Health, and Medical Engineering
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (30 September 2017) | Viewed by 60777
Special Issue Editors
Interests: wearable computing; human–machine–interaction; machine learning; data mining; biomedical engineering; sports engineering
Interests: mobile and personalized health; wearable sensors; machine learning; data analytics; physical and rehabilitation engineering
Special Issue Information
Dear Colleagues,
The application areas of sports, health, and medical engineering are experiencing a remarkable transformation that is driven by digital technologies. Among the most important drivers of this transformation are novel wearable computing systems, as well as machine learning and signal processing algorithms.
Wearable sensors play an increasingly important role in these application areas of sports, health, and medicine. Wearable computing systems can provide real-time feedback and coaching advice. They also allow to instrument studies outside the lab, facilitating the assessment of the real, “in-the-wild”, situation.
Machine learning and signal processing algorithms provide data-driven methods for analyzing the considerable amount of data that is generated in the mentioned application areas. The methods can deal with large data sets, analyze multiple dimensions simultaneously, and provide valuable insights into processes like training effects, health benefits, and chronic disease progression.
The purpose of this Special Issue is to address key topics in wearable computing and machine learning for applications in sports, health, and medical engineering. We are looking forward to impactful manuscripts that address key methodological issues in the aforementioned application fields.
Dr. Bjoern Eskofier
Dr. Sunghoon Ivan Lee
Guest Editors
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Keywords
- Wearable Computing
- Body Sensor Network
- Human-Machine-Interaction
- Machine Learning
- Data Mining
- Big Data
- Biomechanics
- Digital Health
- Biomedical Engineering
- Sports Engineering
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