Tracking the Consumption of Home Essentials

Abstract

Predictions of people's behaviour increasingly drive interactions with a new generation of IoT services designed to support everyday life in the home, from shopping to heating. Based on the premise that such automation is difficult due to the contingent nature of people's practices, in this work we explore the nature of these contingencies in depth. We have designed and conducted a technology probe that made use of simple linear predictions as a provocation, and invited people to track the life of their household essentials over a two-month period. Through a mixed-method approach we demonstrate the challenges of simple predictions, and in turn identify eight categories of contingencies that influenced prediction accuracy. We discuss strategies for how designers of future predictive IoT systems may take the contingencies into account by removing, hiding, revealing, managing, or exploiting the system uncertainty at the core of the issue. CCS CONCEPTS • Human-centered computing → Field studies; Empirical studies in ubiquitous and mobile computing; Empirical studies in HCI .

Smart home technology—comparing householder expectations at the point of installation with experiences 1 year later

“…Although technology progresses, its use and subsequent acceptance or rejection are influenced by the match between expectations and experience. As well as the issue of expectations vs. reality, the actual difficulties faced by our participants are consistent with users of recent technology and other types of SHT such as internet-connected cameras, smart lights, or smart thermostats [36,37]. Recent developments in SHT, for example with the Nest thermostat, are pursuing the simplification of user interactions through algorithms that learn occupancy patterns and energy use [38].…”

Trust your guts: fostering embodied knowledge and sustainable practices through voice interaction

“…Moreover, research that focused on improving fridge management [46] and giving shelf life reminders [48] observed more awareness and hints for a reduction in food waste. Still, managing the inventory by hand and tracking consumed goods are tedious tasks that might not solve the problem in the long term [49].…”

The cardboard box study: understanding collaborative data management in the connected home

“…We then turned to the Internet to identify IoT devices and products for the home and the data they generate, including the Internet of Useless Things [38], the Internet of Shit [37] and Postscapes [56], which allowed us to identify a broad range of connected devices that would generate data about everyday activities across the home. We also took research that we knew of into account, such as the Predictive Shopping List [20], the Living Room of the Future [59] and the Connected Shower [15]. We categorised devices and data into different types, online, wearable, environmental, smart products and appliances, vehicles, etc., as a means of collating the results of our survey (we use the term loosely) and parsing the rather large collection of discrete data sources finding their way into the connected home.…”