- Aalto University
- Maarintie 8
- 00076 Espoo
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.
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