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Muneeba Raja

Muneeba Raja

  • Aalto University
  • Maarintie 8
  • 00076 Espoo
  • Finland
  • muneeba.raja@aalto.fi

My research interests cover RF-based device-free activity recognition, in particular sentiment-indicators. The objective is to exploit the capabilities of RF-signals, say Wi-Fi to detect and learn human behaviour, emotions and attention levels. Radio signals are ubiquitous, cheap, easily deployable and less privacy intrusive as compared to video cameras and physiological sensors.
Currently, I am using this approach to find the attention levels of a driver in car (autonomous and non-autonomous). This is done by detecting activities, body (arm and head) movements captured by Wi-Fi Signals. Finding head orientations (frequency, direction and duration) gives information about the state of the driver, e.g., if he is lost, found some interesting landmarks etc. The activities with arms and hands, for example, texting, eating, drinking, fetching, using multimedia box also gives information about how an individual driver behaves while in car. This information can be used to either give warnings/feedback depending on his state. In highly autonomous vehicles, where driver is expected to utilise his time by doing everyday tasks while in car, this information can help learn routines, likes, dislikes of the driver and providing him better services. Moreover, particular gestures detection can be used as a remote control feature for accessories in car.
Currently Wi-Fi is already available in modern cars, using video cameras to obtain this information would be highly intrusive and the processing of image data would be computationally expensive.
The human study for this use case has been carried out for 45 participants at BMW research centre, Munich. Now the challenge of accurately distinguishing between arm movements, head movements, interesting activities and gestures is being carried out. The results will be published in upcoming conferences.

Publications

  2019 (1)
Wireless Multi-frequency Feature Set to Simplify Human 3D Pose Estimation. Raja, M.; Hughes, A.; Xu, Y.; zarei , P.; Michelson, D. G.; and Sigg, S. IEEE Antennas and Wireless Propagation letters, 18(5): 876-880. 2019.
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  2018 (3)
A cloud-IoT platform for passive radio sensing: challenges and application case studies. Kianoush, S.; Raja, M.; Savazzi, S.; and Sigg, S. IEEE Internet of Things Journal. 2018.
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WiBot! In-Vehicle Behaviour and Gesture Recognition Using Wireless Network Edge. Raja, M.; Ghaderi, V.; and Sigg, S. In 38th IEEE International Conference on Distributed Computing Systems (ICDCS 2018), 2018.
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Detecting Driver's Disracted Behaviour from Wi-Fi. Raja, M.; Ghaderi, V.; and Sigg, S. In Vehicular Technology Conference (vtc 2018-spring), 2018.
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  2017 (3)
Towards pervasive geospatial affect perception. Raja, M.; Exler, A.; Hemminki, S.; Konomi, S.; Sigg, S.; and Inoue, S. Springer GeoInformatica. 2017.
Towards pervasive geospatial affect perception [link]Paper   doi   bibtex   abstract
RFexpress! – Exploiting the wireless network edge for RF-based emotion sensing. Raja, M.; and Sigg, S. In 22nd IEEE International Conference on Emerging Technologies And Factory Automation (ETFA'17), 2017.
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RFexpress! - RF Emotion Recognition in the Wild. Raja, M.; and Sigg, S. In 2017 IEEE International Conference on Pervasive Computing and Communication (WiP), March 2017.
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  2016 (1)
Applicability of RF-based methods for emotion recognition: A survey. Raja, M.; and Sigg, S. In 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pages 1-6, March 2016.
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Maarintie 8, 00076 Espoo; +358 (0)50 4666941; ambience-elec@aalto.fi