UBISTRUCT Living Lab
An experimental platform dedicated to the implementation of reasoning models and activity recognition algorithms and ambient user support services. Context awareness is of paramount importance and focusing on multi modal recognition of users' situations and activities. We use for that various sensing technologies and geo-positioning systems such as Kricket (Ultra sound combined with radio), Wifi Finger printing, RSSI based triangulation and trilateration with Zigbee or 6lowPan WSN or the new the newly bluetooth low energy, which is still under investigation.
In the living lab premisses are deployed more several wireless sensors and actuators networks that comply with the standards of the Internet of Things such as RFID, Bluetooth BLE, Zwave, Zigbee and 6lowpan. The living lab includes also geo-positioning systems based on Cricket and Ibeacon technologies. 7 mobile companion robots, compliant with ROS, are made available for applications designers and researchers including 4 Aldebaran SoftBank pepper robots, two Willow garage ROS Turtlebot robots and one Robosoft Kompai V1 robot.
In case your are interested to validate your research works in our experimentation facility or join our research and development teams, please fill the contact form bellow
References:
[1] F. Attal, Y. Amirat, A. Chibani, and S. Mohammed, "Automatic Recognition of Gait phases Using a Multiple Regression Hidden Markov Model," IEEE/ASME Transactions on Mechatronics, 2018. <10.1109/TMECH.2018.2836934>. .
[2] H. Abdelkawy, N. Ayari, A. Chibani, Y. Amirat, and F. Attal, "Deep HMResNet Model for Human Activity-Aware Robotic Systems," in Proc. of the AAAI 2018 Fall Symposium Series, Arlington, United States, Oct. 2018.
[3] H. Abdelkawy, S. Fiorini, A. Chibani, N. Ayari, and Y. Amirat, "Deep CNN and Probabilistic DL Reasoning for Contextual Affordances," in Proc. of the AAAI 2018 Fall Symposium Series, Arlington, United States, Oct. 2018. .
[4] F. Sebbak, S. Bouznad, F. Benhammadi, A. Chibani, and Y. Amirat, "Context Awareness in Uncertain Pervasive Computing and Sensors Environment," in Proc. Of the 21th International conference on information fusion, FUSION 2018, Cambridge, United Kingdom, Jul. 2018. .
[5] S. Bouznad, A. Chibani, Y. Amirat, L. Sabri, E. Prestes, F. Sebbak, and S. Fiorini, "Context-Aware Monitoring Agents for Ambient Assisted Living
Applications," in Proc. Of the 13th European Conference on Ambient Intelligence, AmI 2017, Malaga, Spain, 2017, pp. 225-240. .
[6] N. Ayari, A. Chibani, Y. Amirat, and E. Matson, "A Semantic Approach for Enhancing Assistive Services in ubiquitous robotics," Robotics and Autonomous Systems, Elsevier, vol. 75, pp. 17-27, 2016. .
[7] S. Bouznad, F. Sebbak, F. Benhammadi, Y. Amirat, and A. Chibani, "Generalized Fuzzy Soft Set Based Fusion Strategy for Activity Classification in Smart Home," in Proc. Of the IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2017, Naples, Italy, Jul. 2017, pp. 1-6. .
[8] S. Bouznad, F. Sebbak, F. Benhammadi, Y. Amirat, and A. Chibani, "Multi-observer Decision Making Approach Using Power Fuzzy Soft Sets," in Proc. Of the IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2017, Naples, Italy, Jul. 2017, pp. 1-6. .
[9] F. Sebbak, F. Benhammadi, S. Bouznad, A. Chibani, and Y. Amirat, "An Evidential Fusion Rule for Ambient Intelligence for Activity Recognition," in Proc. Of the 3rd International Conference on Belief Functions (BELIEF 2014),, Oxford, United Kingdom, 2014, pp. 356-364. .
[10] F. Sebbak, A. Chibani, Y. Amirat, A. Mokhtari, and F. Benhammadi, "An Evidential Fusion Approach for Activity Recognition in Ambient Intelligence Environments," Robotics and Autonomous Systems, vol. 61, no. 11, pp. 1235-1245, 2013. .
[11] F. Sebbak, F. Benhammadi, A. Mokhtari, A. Chibani, and Y. Amirat, "Evidence Combination Based on CSP Modeling," in Proc. Of the 16th International conference on information fusion, FUSION 2013, Istanbul, Turkey, 2013, pp. 1111-1118. .
[12] F. Sebbak, A. Chibani, Y. Amirat, A. Mokhtari, and F. Benhammadi, "An evidential fusion approach for activity recognition under uncertainty in ambient intelligence environments," in Proc. Of the 14th International conference on Ubiquitous Computing, UbiComp'12, Pittsburgh, United States, 2012, pp. 834-840. .