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News and Resources for Members of the IEEE Signal Processing Society
In this series, we introduce a scientist, who uses signal processing techniques for his research findings, by means of an interview. This month, we are happy to introduce Dr. Jorge Arroyo Palacios, a postdoctoral scholar at Department of Physiological Nursing, University of California, San Francisco, whose research mainly focuses on physiological signal processing.
What are your research interests in the signal processing field?
My research aims to explore the use of physiological signals and machine learning methods to design and implement innovative intelligent computer systems able to: i) support healthcare practitioners and patients, ii) adapt to the users’ health, physical and affective state, and iii) interact with the users in a natural and intuitive way. In particular my research interests spans over the following areas: Physiological Computing, Smart Patient Monitoring, Affective Computing, and Virtual Reality.
Could you briefly introduce your research?
My current research at UCSF involves physiological signal processing, algorithm development and validation, and machine learning to solve clinically significant problems such as designing new weaning protocols of intracranial pressure monitoring of brain injury patients.
During my PhD at the University of Sheffield, UK, I developed a Bio-Affective Computer Interface that recognizes emotions in real-time and facilitates the adaptation of third party applications. In addition, I explored an innovative way to interact with computer applications by designing and developing a respiratory computer interface (RCI).
After my PhD, as a postdoctoral researcher at the EventLab, University of Barcelona, I continued this line of research in physiological computing interacting systems using immersive virtual reality. My research included: i) avatar-human emotional contagion in mobile platforms, ii) interaction with avatars using physiological signals and brain-computer interfaces, iii) the use of physiological signals to enhance the sense of embodiment in avatars, iv) physiological and emotional effects of virtual embodiment, v) reinforcement learning to maximize the sense of presence in immersive virtual reality.
In your opinion, what was the most impressive result published in IEEE SPS journals and conferences within the last 12 months?
A recent publication from the IEEE SPS journals that was very interesting for me was “Cooperative Learning and its Application to Emotion Recognition from Speech” by Zhang et al., published on IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 23, No. 1, 2015, DOI: 10.1109/TASLP.2014.2375558. This paper proposed Cooperative Learning as a novel approach for exploitation of unlabeled speech data by sharing the labeling work between human and machine. The results from their tests show that Cooperative Learning outperforms individual Active and Semi-Supervised Learning techniques.
Could you introduce an important state-of-the-art research issue (or technology) in this field (Other than your research)?
The efforts of several startups and the recent focus of big companies like Google, Apple and Samsung on wearable devices, physiological sensors and software platforms (such as Google Fit and Apple HealthKit), offer a promising panorama for the development of areas such as mobile health, smart patient monitoring, natural human-computer interfaces. I believe wearable devices will have an important impact in the field of signal processing in the near future, influencing the way signals are gathered, generated, transformed and interpreted.
In which way have you been connected first with IEEE (university, conference, etc…)?
My first contact with IEEE was in the IEEE Consumer Electronics Society's Games Innovations Conference where I presented a paper about the exploration of an alternative approach to interact with games using respiratory signals.
In which way did you know the IEEE SPS e-NewsLetter?
I have been introduced to the IEEE SPS e-News Letter by my colleagues at UCSF who work with Big Clinical Data, physiological signal modeling and machine learning.
Jorge Arroyo-Palacios graduated from the Instituto Tecnológico de Ciudad Victoria in 2003 with a BEng degree in Computer Systems. He awarded a research scholarship from the Mexican Research Council of Science and Technology (CONACYT) to continue his postgraduate studies. He received his MSc degree in Advanced Computer Science in 2006 and his PhD in Computer Science in 2011 from the University of Sheffield, UK. Currently he is working as a Postdoctoral Scholar at the University of California, San Francisco. His research aims to explore the use of physiological signals and machine learning methods to design and implement innovative intelligent computer systems able to: i) adapt to the users’ health, physical and affective state, ii) interact with the users in a natural and intuitive way, and iii) support healthcare practitioners and patients.
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