Designing Natural Language Output for the IoT

A large number of devices categorised as "Internet of Things" (IoT) that are in the consumer market are designed to autonomously monitor things of interest to users. These devices often make use of natural language output, more specifically textual messages, as a way to notify users. These messages are commonly simple predetermined strings. Some IoT devices however are designed to report on complex applications, which may be difficult for users without technical domain knowledge to understand. In this work, we present an initial evaluation in which we investigated how users' inclination to attend to a monitoring system is affected by different levels of information. Based our findings, we discuss future avenues of research which we believe will further our understanding of natural language output's application in the IoT domain

Evaluating the Effect of Feedback from Different Computer Vision Processing Stages: A Comparative Lab Study

Computer vision and pattern recognition are increasingly being employed by smartphone and tablet applicationstargeted at lay-users. An open design challenge is to make such systems intelligible without requiring users to become technical experts. This paper reports a lab study examining the role of visual feedback. Our fndings indicate that the stage of processing from which feedback is derived plays an important role in users’ ability to develop coherent and correct understandings of a system’s operation. Participants in our study showed a tendency to misunderstand the meaning being conveyed by the feedback, relating it to processing outcomes and higher level concepts, when in reality the feedback represented low level features. Drawing on the experimental results and the qualitative data collected, we discuss the challenges of designing interactions around pattern matching algorithms

Camera-based window-opening estimation in a naturally ventilated office

Naturally ventilated offices enable users to control their environment through the opening of windows. Whilst this level of control is welcomed by users, it creates risk in terms of energy performance, especially during the heating season. In older office buildings, facilities managers usually obtain energy information at the building level. They are often unaware or unable to respond to non-ideal facade interaction by users often as a result of poor environmental control provision. In the summer months, this may mean poor use of free cooling opportunities, whereas in the winter space heating may be wasteful. This paper describes a low-cost, camera-based system to diagnose automatically the status of each window (open or closed) in a facade. The system is shown to achieve a window status prediction accuracy level of 90–97% across both winter and summer test periods in a case study building. A number of limitations are discussed including winter daylight hours, the impact of rain, and the use of fixed camera locations and how these may be addressed. Options to use this window-opening information to engage with office users are explored

Evaluating the effect of feedback from different computer vision processing stages: a comparative lab study

Computer vision and pattern recognition are increasingly being employed by smartphone and tablet applications targeted at lay-users. An open design challenge is to make such systems intelligible without requiring users to become technical experts. This paper reports a lab study examining the role of visual feedback. Our findings indicate that the stage of processing from which feedback is derived plays an important role in users' ability to develop coherent and correct understandings of a system's operation. Participants in our study showed a tendency to misunderstand the meaning being conveyed by the feedback, relating it to processing outcomes and higher level concepts, when in reality the feedback represented low level features. Drawing on the experimental results and the qualitative data collected, we discuss the challenges of designing interactions around pattern matching algorithms

Camera-based window-opening estimation in a naturally ventilated office

Naturally ventilated offices enable users to control their environment through the opening of windows. Whilst this level of control is welcomed by users, it creates risk in terms of energy performance, especially during the heating season. In older office buildings, facilities managers usually obtain energy information at the building level. They are often unaware or unable to respond to non-ideal facade interaction by users often as a result of poor environmental control provision. In the summer months, this may mean poor use of free cooling opportunities, whereas in the winter space heating may be wasteful. This paper describes a low-cost, camera-based system to diagnose automatically the status of each window (open or closed) in a facade. The system is shown to achieve a window status prediction accuracy level of 90–97% across both winter and summer test periods in a case study building. A number of limitations are discussed including winter daylight hours, the impact of rain, and the use of fixed camera locations and how these may be addressed. Options to use this window-opening information to engage with office users are explored