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Research focus:
Developing and applying machine learning and time series analysis methods for (sensor-based) user monitoring & modelling, including the following topics:
- Deep learning approaches for (physiological) user monitoring.
- Time series analysis methods for wearable and ubiquitous computing
- Machine learning algorithms, with special interest in ensemble learners (boosting, adaptive majority voting, etc)
- Personalisation algorithms
- Spatio-temporal prediction of user behaviour, user behaviour modelling
- Affect and stress recognition
- Activity and gesture recognition, spotting
- Design and execution of user studies and user experiments
Topics of interest:
machine learning, deep learning, time series analysis, wearable computing, ubiquitous computing, sensor technology, physiological monitoring, affective computing, pervasive healthcare, smart home
Developing and applying machine learning and time series analysis methods for (sensor-based) user monitoring & modelling, including the following topics:
- Deep learning approaches for (physiological) user monitoring.
- Time series analysis methods for wearable and ubiquitous computing
- Machine learning algorithms, with special interest in ensemble learners (boosting, adaptive majority voting, etc)
- Personalisation algorithms
- Spatio-temporal prediction of user behaviour, user behaviour modelling
- Affect and stress recognition
- Activity and gesture recognition, spotting
- Design and execution of user studies and user experiments
Topics of interest:
machine learning, deep learning, time series analysis, wearable computing, ubiquitous computing, sensor technology, physiological monitoring, affective computing, pervasive healthcare, smart home
研究兴趣
论文共 39 篇作者统计合作学者相似作者
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user-5d4bc4a8530c70a9b361c870(2021)
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Proceedings of the 23rd International Symposium on Wearable Computers (2019)
Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (2018)
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