Physiological Computing Systems
International Conferences, PhyCS 2016, Lisbon, Portugal, July 27–28, 2016, PhyCS 2017, Madrid, Spain, July 27–28, 2017, PhyCS 2018, Seville, Spain, September 19–21, 2018, Revised and Extended Selected Papers
Nonfiction, Computers, Advanced Computing, Programming, Expert Systems, General Computing, Health & Well Being, Medical
| Author: | ISBN: | 9783030279509 | |
| Publisher: | Springer International Publishing | Publication: | August 27, 2019 |
| Imprint: | Springer | Language: | English |
| Author: | |
| ISBN: | 9783030279509 |
| Publisher: | Springer International Publishing |
| Publication: | August 27, 2019 |
| Imprint: | Springer |
| Language: | English |
This book constitutes the proceedings of the Third International Conference on Physiological Computing Systems, PhyCS 2016, held in Lisbon, Portugal, in July 2016.
The 12 papers presented in this volume were carefully reviewed and selected from numerous submissions. They contribute to the understanding of relevant trends of current research on physiological computing systems, including brain-computer interfaces, virtual reality, psychophysiological load assessment in unconstrained scenarios, body tracking and movement pattern recognition, emotion recognition, machine learning applied to diabetes and hypertension, tangible biofeedback technologies, multimodal sensor data fusion, and deep learning for hand gesture recognition.
This book constitutes the proceedings of the Third International Conference on Physiological Computing Systems, PhyCS 2016, held in Lisbon, Portugal, in July 2016.
The 12 papers presented in this volume were carefully reviewed and selected from numerous submissions. They contribute to the understanding of relevant trends of current research on physiological computing systems, including brain-computer interfaces, virtual reality, psychophysiological load assessment in unconstrained scenarios, body tracking and movement pattern recognition, emotion recognition, machine learning applied to diabetes and hypertension, tangible biofeedback technologies, multimodal sensor data fusion, and deep learning for hand gesture recognition.
