Frame-based elicitation of mid-air gestures for a smart home device ecosystem
P Vogiatzidakis, P Koutsabasis - Informatics, 2019 - mdpi.com
Informatics, 2019•mdpi.com
If mid-air interaction is to be implemented in smart home environments, then the user would
have to exercise in-air gestures to address and manipulate multiple devices. This paper
investigates a user-defined gesture vocabulary for basic control of a smart home device
ecosystem, consisting of 7 devices and a total of 55 referents (commands for device) that
can be grouped to 14 commands (that refer to more than one device). The elicitation study
was conducted in a frame (general scenario) of use of all devices to support contextual …
have to exercise in-air gestures to address and manipulate multiple devices. This paper
investigates a user-defined gesture vocabulary for basic control of a smart home device
ecosystem, consisting of 7 devices and a total of 55 referents (commands for device) that
can be grouped to 14 commands (that refer to more than one device). The elicitation study
was conducted in a frame (general scenario) of use of all devices to support contextual …
If mid-air interaction is to be implemented in smart home environments, then the user would have to exercise in-air gestures to address and manipulate multiple devices. This paper investigates a user-defined gesture vocabulary for basic control of a smart home device ecosystem, consisting of 7 devices and a total of 55 referents (commands for device) that can be grouped to 14 commands (that refer to more than one device). The elicitation study was conducted in a frame (general scenario) of use of all devices to support contextual relevance; also, the referents were presented with minimal affordances to minimize widget-specific proposals. In addition to computing agreement rates for all referents, we also computed the internal consistency of user proposals (single-user agreement for multiple commands). In all, 1047 gestures from 18 participants were recorded, analyzed, and paired with think-aloud data. The study reached to a mid-air gesture vocabulary for a smart-device ecosystem, which includes several gestures with very high, high and medium agreement rates. Furthermore, there was high consistency within most of the single-user gesture proposals, which reveals that each user developed and applied her/his own mental model about the whole set of interactions with the device ecosystem. Thus, we suggest that mid-air interaction support for smart homes should not only offer a built-in gesture set but also provide for functions of identification and definition of personalized gesture assignments to basic user commands.
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