ARQ - Advanced Robotics at Queen Mary

Events

Wearable Sensing and Medical Big Data for Precision Health

by Professor Ye Li, Shenzhen Institute of Advanced Technologies, China

http://www.bit-siat.com/en/
https://scholar.google.co.uk/citations?user=Vo09I7kAAAAJ

 

When: Monday, December 16, 2019, 10:00-10:40

Where: Mile End campus, Bancroft ROAD teaching rooms, 3rd floor, 3.02 (not to be confused with the Bancroft Building, which is on the other side of the campus)

Abstract: Cardiovascular disease is one of the most significant diseases in China. We work on wearable sensing and medical big data to prevent it. In our previous studies, we proposed the AI-driven cuff-less blood pressure detection based on data fusion for wearable applications. We also developed the low power ECG and EMG chips for wearable devices and remote controls. The spatial-temporal deep neural network for ECG signals was proposed to improve the accuracy of automatic diagnosis of 9 types of cardiac arrhythmias. The developed wearable system has been applied to monitoring the life signal of submerged members in deep manned submersibles and served for more than 3.62 million family users in China. In the area of medical big data analysis, we processed 18 M electronic medical records (EMR) in Shenzhen city, and several cardiovascular disease risk models were developed to provide  decision-making aided information for public health management in Shenzhen.

Bio: Professor Ye Li is a world-renowned expert on wearable medical devices and human body sensing. He has gained more than USD 12M in research funding and has authored over 60 peer-reviewed journal papers. Currently, he serves as an editorial board member of Information Fusion journal and international advisory board member of Physiological Measurement journal. He got his Ph.D in Electronic Engineering from Arizona State University, USA, and his MSc and BSc in Information security and engineering from University of Electronic Science and Technology of China.

Interactive Agri-Robotics and Machine Learning for Machine Listening

by Philip Noman, Ross Robotics, UK

and Dr Emmanouil Benetos, Queen Mary University of London, UK

 

When: Fri, Nov 22, 12:30-15:00

Where: ArtsOne, Room 1.28

Interactive Agri-Robotics: Philip Norman will present a case study of modular robot for poultry farming, with potential implications for other sectors. The talk will focus on interactivity in data collection, analytics and better informed decision-making for improved animal welfare/commercial outcomes and animal/robot interactions to modify individual and group behaviour for improved welfare/commercial outcomes.

Machine Learning for Machine Listening: Audio analysis -also called machine listening- involves the development of algorithms capable of extracting meaningful information from audio signals such as speech, music, or environmental sounds, typically drawing knowledge from the fields of digital signal processing and artificial intelligence. Machine listening applications are numerous, including but not limited to smart homes/smart cities, ambient assisted living, biodiversity assessment, security/surveillance and audio archive management amongst others. The talk will outline recent research carried out at QMUL that focuses on sound recognition in complex acoustic environments, inspired by and proposing new methods in the area of machine learning. Topics covered will include designing new learning objectives for audio analysis, domain and context adaptation for audio, methods for interpretability in machine listening, and studies on the robustness of machine listening methods.

Seminar: Perspectives on robotics interaction control

by Dr Thiago BoaventuraSao Paulo University, Brazil

 

When: Wed, April 17, 16:00-17:00

Where: Computer Science building (entrance from Bancroft road), room 3.02, 3rd floor

Abstract: Very often robots have to physically interact with the environment, people, tools, or other objects. To properly control such interactions, it is important to be able to control both the forces applied by the robot as well as its displacement. In this talk, a few perspectives on how to control such physical interactions will be presented for two different robots: a quadruped and an exoskeleton. Relevant aspects such as the stability and certification of these controllers will also be discussed.

Bio: Thiago Boaventura received the B.Sc. and M.Sc. degrees in mechatronic engineering from the Federal University of Santa Catarina, Florianopolis, Brazil, in 2009. From 2010 to 2013 he worked at the Italian Institute of Technology, Genoa, Italy, on the control of the hydraulic quadruped robot HyQ. In 2013 he got his the Ph.D. degree in robotics, cognition, and interaction technologies from University of Genoa, Italy. Then, he was a postdoctoral researcher for 4 years with Agile and Dexterous Robotics Laboratory, ETH Zurich, Switzerland, involved mainly in the EU FP7 BALANCE project with a focus on the collaborative impedance control of exoskeletons. Since 2017 Thiago is an Assistant Professor at the University of Sao Paulo. His research interests include impedance and admittance control, model-based control, legged robotics, optimal and learning control, and wearable robotics.

TAROS 2019

REGISTRATION IS NOW OPEN

GET THE PROGRAMME HERE

FREE ACCESS TO PROCEEDINGS DURING THE CONFERENCE

 

The 20th TAROS conference will be hosted by the Centre for Advanced Robotics @ Queen Mary, Queen Mary University of London from the 3rd to the 5th of July 2019.

Camera-ready submission deadline for both short and full-length papers is the 26th of April, 2019. The submission of manuscripts is via Springer conference submission system (Springer OCS).

TAROS is the longest running UK-hosted international conference on Robotics and Autonomous Systems (RAS), which is aimed at the presentation and discussion of the latest results and methods in autonomous robotics research and applications.

TAROS offers a friendly environment for robotics researchers and industry to take stock and plan future progress. It welcomes senior researchers and research students alike, and specifically provides opportunities for research students and young research scientists to present their work to the scientific community.