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In order to improve the interactivity between users and computers, recent technologies focus on incorporating gesture recognition into interactive systems. The aim of this article is to evaluate the effectiveness of using a Myo control armband and the Kinect 2 for recognition of gestures in order to interact with virtual objects in a weather report scenario. The Myo armband has an inertial measurement unit and is able to read electrical activity produced by skeletal muscles, which can be recognized as gestures, which are trained by machine learning. A Kinect sensor was used to build up a dataset which contains motion recordings of 8 different gestures and was also build up by a gesture training machine learning algorithm. Both input methods, the Kinect 2 and the Myo armband, were evaluated with the same interaction patterns in a user study, which allows a direct comparison and reveals benefits and limits of each technique.
The interpretation process of complex data sets makes the integration of effective interaction techniques crucial. Recent work in the field of human-computer interaction has shown that there is strong evidence that multimodal user interaction, i.e. the integration of various input modalities and interaction techniques into one comprehensive user interface, can improve human performance when interacting with complex data sets. However, it is still unclear which factors make these user interfaces superior to unimodal user interfaces. The contribution of this work is an analytical comparison of a multimodal and a unimodal user interface for a scientific visualization application. We show that multimodal user interaction with simultaneously integrated speech and gesture input improves user performance regarding efficiency and ease of use.