Multimodal Elderly Care Systems (MECS)
About the project
The number of elderly people living at home is increasing and this trend is expected to continue. The challenge then would be how to provide technology that can handle the complex and different environments found in homes. Further, technology can easily be seen as a threat to privacy and lack of interpersonal contact. This project addresses these issues by user centered design of robotic systems and the development of adaptive technology. A part of this will be to demonstrate the benefits regarding both performance and privacy being improved by applying sensors like cameras on a robot companion rather than having them permanently mounted in a home. These would be used for detecting falls and other non-normal situations. Using new sensor technology, we would also like to explore if it is possible to remotely monitor medical states like pulse, breathing etc. Rather than having elderly themselves activating their personal security alarm in the case of an emergency situation, a target of this project is to demonstrate automatic activation. Many systems for elderly have been designed but few have been adopted on a large scale. We think a key reason for this is limited user involvement and few iterations of user testing. Therefore, we will focus specifically on developing our systems with a large degree of user participation.
In this project, a robot companion moves in a residential environment in the presence of unknown static and dynamic obstacles. Thus, having an effective and efficient control architecture, plays an important role in the achievement of the project objectives.
Create and evaluate multimodal mobile human supportive systems that are able to sense, learn and predict future events.
Research Council of Norway 2015–2019, IKTPLUSS, One PhDs and two post-doc (10% of proposals in the call were funded)
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- Diana Saplacan & Jo Herstad (2018). A Quadratic Anthropocentric Perspective on Feedback - Using Proxemics as a Framework, In Phil Turner; Tom Flint; Lynn Hall & Suzy O’Hara (ed.), Proceedings of the 31st International BCS Human Computer Interaction Conference (HCI 2017). British Computer Society (BCS). ISBN 9781906124045. 1.
- Md Zia Uddin; Jim Tørresen & Taskeed Jabid (2017). Human Activity Recognition using depth body part histograms and Hidden Markov Models, In Mohammad Kaykobad (ed.), 2016 International Conference on Innovations in Science, Engineering and Technology (ICISET). IEEE conference proceedings. ISBN 978-1-5090-6123-5. Paper ID: 168. s 151 - 154
- Md Zia Uddin; Weria Khaksar & Jim Tørresen (2017). Human activity recognition using robust spatiotemporal features and convolutional neural network, In Uwe D. Hanebeck (ed.), 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE conference proceedings. ISBN 978-1-5090-6064-1. Paper No.: T2C-2. s 144 - 149
- Md Zia Uddin; Weria Khaksar & Jim Tørresen (2017). A robust gait recognition system using spatiotemporal features and deep learning, In Uwe D. Hanebeck (ed.), 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE conference proceedings. ISBN 978-1-5090-6064-1. Paper No.: T2C-4. s 156 - 161
- Asbjørn Danielsen & Jim Tørresen (2017). Recognizing Bedside Events Using Thermal and Ultrasonic Readings. Sensors. ISSN 1424-8220. 17
- Weria Khaksar; Tang Sai Hong; Khairul Salleh Mohamed Sahari; Mansoor Khaksar & Jim Tørresen (2017). Sampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference system. Neural computing & applications (Print). ISSN 0941-0643. Published ahead of print, s 1- 15
- Md Zia Uddin; Mohammed Mehedi Hassan; Ahmad Almogren; Mansour Zuair; Giancarlo Fortino & Jim Tørresen (2017). A facial expression recognition system using robust face features from depth videos and deep learning. Computers & electrical engineering. ISSN 0045-7906. 63, s 114- 125
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- Trenton Wade Schulz (2017). A Literature Review of Animation in Robot Motion.
- Jim Tørresen (2017). Sensing and Reasoning Technology Applied within Mental Health Treatment and Elderly Care Robot Companions.
- Jim Tørresen (2017). Robotics activity at Robotics and Intelligent Systems Group University of Oslo.
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- Jim Tørresen (2017). Artificial Intelligence – What is it and what can it be applied to?.
- Rebekka Soma (2017). Making sense of human-robot encounters.
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- Trenton Wade Schulz (2017). Presentation: Walking Away from the Robot: Negotiating Privacy with a Robot.
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- Tannaz Navaie Roshandel (2017). System Control for Safe Autonomous Navigation of Robot Systems for Elderly Care.
- Lexu Qi (2017). Using Skeleton Information for Human Identification for Elderly Care and Alarm System with the XBox One Kinect Sensor.
- Magnus Søyland & Vegard Dønnem Søyseth (2017). “Hey, I’m walking here!” An explorative study of spatial encounters between older adults and autonomous robots.
- Jim Tørresen (2016). Artificial intelligence in autonomous systems.
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- (2016). Eldreomsorg med roboter.
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