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Gesture Recognition on Wearable Devices (B)

Status: Abgeschlossen


Wearable devices make technology pervasive by seamlessly integrating it into our daily life. From smart watches, to fitness trackers and smart glasses, many companies are currently involved in releasing new iterations of their devices, which become smaller, more powerful, and are equipped with more sensors. In this thesis we are interested in a particular category of wearable devices, the smartwatch. Smartwatches have two strong advantages over other devices: location (wrist worn) and continuous contact with the skin.

Goals and challenges

Since smartwatches are wrist worn, users’ hand and arm movement can be continuously monitored and tracked. By identifying certain gestures we can create new applications for single users like controlling a nearby television, interacting with toys, or computers. Further, the case of multiple users in the same area triggers interesting questions. How can multi-user gestures and activity recognition result in collaborative and interactive experiences? Examples of applications are playing games, controlling interactive installations or public displays and many others.


  • Solid programming skills (Android, C/C++ preferred)
  • Background in signal processing is necessary
  • Basic machine learning background

Student/Bearbeitet von: Sander Staal
Contact/Ansprechpartner: Mihai Bâce, Gábor Sörös

ETH ZurichDistributed Systems Group
Last updated September 9 2016 10:19:30 AM MET mb