Difference between revisions of "FirstResponse"
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Simin Wang, Salim Zabir, Bastian Leibe. Lying Pose Recognition for Elderly Fall Detection. Proceedings of Robotics: Science and Systems 2011. | Simin Wang, Salim Zabir, Bastian Leibe. Lying Pose Recognition for Elderly Fall Detection. Proceedings of Robotics: Science and Systems 2011. | ||
|Prerequisites=Image analysis, sensors systems, learning systems, cooperating intelligent systems or similar | |Prerequisites=Image analysis, sensors systems, learning systems, cooperating intelligent systems or similar | ||
| − | |Supervisor=Anita Sant'Anna, | + | |Supervisor=Anita Sant'Anna, |
|Examiner=Antanas Verikas | |Examiner=Antanas Verikas | ||
| + | |Author=Gloria | ||
|Level=Master | |Level=Master | ||
|Status=Ongoing | |Status=Ongoing | ||
}} | }} | ||
Smart homes and ambient assisted living applications can support healthy and safe aging at home. One important safety feature is to be able to detect an emergency situation and take appropriate actions in a timely manner. Mobile robots can complement smart homes with additional mobile sensors such as cameras and Kinect sensors. These robots have the ability to more closely investigate a potential emergency situation such as a fall; initiate interaction with the person who has fallen; and decide on what actions to take given the response or lack of response from the person. This project will investigate different sensor modalities and methods to detect falls and potential emergency situations, as well as human-robot interaction modalities; and emergency response actions to ensure the person’s safety. | Smart homes and ambient assisted living applications can support healthy and safe aging at home. One important safety feature is to be able to detect an emergency situation and take appropriate actions in a timely manner. Mobile robots can complement smart homes with additional mobile sensors such as cameras and Kinect sensors. These robots have the ability to more closely investigate a potential emergency situation such as a fall; initiate interaction with the person who has fallen; and decide on what actions to take given the response or lack of response from the person. This project will investigate different sensor modalities and methods to detect falls and potential emergency situations, as well as human-robot interaction modalities; and emergency response actions to ensure the person’s safety. | ||
Revision as of 12:01, 13 January 2015
| Title | FirstResponse |
|---|---|
| Summary | First response to emergency situation in a smart environment using a mobile robot |
| Keywords | Image analysis, camera, mobile robot, falls, human-robot interaction, emergency response |
| TimeFrame | 2014-02-01 / 2014-05-31 |
| References | Loreto Susperregi, et al. On the Use of a Low-Cost Thermal Sensor to Improve Kinect People Detection in a Mobile Robot. Sensors 2013, 13(11), 14687-14713.
Simin Wang, Salim Zabir, Bastian Leibe. Lying Pose Recognition for Elderly Fall Detection. Proceedings of Robotics: Science and Systems 2011. |
| Prerequisites | Image analysis, sensors systems, learning systems, cooperating intelligent systems or similar |
| Author | Gloria |
| Supervisor | Anita Sant'Anna |
| Level | Master |
| Status | Ongoing |
Smart homes and ambient assisted living applications can support healthy and safe aging at home. One important safety feature is to be able to detect an emergency situation and take appropriate actions in a timely manner. Mobile robots can complement smart homes with additional mobile sensors such as cameras and Kinect sensors. These robots have the ability to more closely investigate a potential emergency situation such as a fall; initiate interaction with the person who has fallen; and decide on what actions to take given the response or lack of response from the person. This project will investigate different sensor modalities and methods to detect falls and potential emergency situations, as well as human-robot interaction modalities; and emergency response actions to ensure the person’s safety.
