For the MakeZurich hackathon in early February we explored how to combine the Carunda24 smart strap technology and dizmo. Dizmo is the Interface of Things, a next-generation UI platform designed for the Internet of Things (IoT), while the Carunda24 smart strap provides gesture recognition based on a wearable sensor. The hackathon was a great opportunity to test out new ways of approaching data visualization using the technologies of two Swiss startups.
Gesture Recognition and Things
Dizmo fully embraces the concept of natural interfaces, where “things” can be instinctively understood and learned. Dizmo is a very flexible platform and is usually used with a mouse or on a touchscreen. With dizmo, a user can drag elements around, combine IoT device control with data streams, and intuitively interact with complex systems.
At Carunda24, we’ve been developing a gesture recognition system based on a simple wristband with integrated Soft Condensed Matter Sensor, with the vision of using natural hand gestures to interact with robots, drones, and interactive environments (VR/AR). The MakeZurich hackathon provided a good platform to bring the Carunda24 and dizmo technologies together. Previously the Carunda24 smart strap has been used for NUI drone control, and it was interesting to now investigate data visualization interaction.
With hand gestures enabled by the smart strap, the natural interface becomes even more untethered, especially on a horizontal screen or as a projected interface on a table. Combining the two technologies can enable Natural User Interface (NUI) interaction in a local experience, or remotely by leveraging IoT device control.
There were six challenges proposed at the MakeZurich hackathon, our team decided to participate in the Grün Stadt Zürich challenge. Every tree in the city of Zurich that is managed or taken care by Grün Stadt Zürich is included in the Baumkataster, an open data set in GeoJSON that includes around 50,000 trees. A visualization of those trees on the map of Zurich was done by extending the existing Map dizmo with a data layer.