Principles for inclusive smart homes
From the analysis and testing done during this project, three main design principles could be identified. They are insights that are not specifically applicable to the design goals nor to the product that was designed. They are general principles that can be used as guidelines to design more inclusive smart home products. This chapter introduces these guidelines and uses the wake-up light design scenario to explain them, see below.
Domestication of technology is the process with which people hear about a technology, learn to use it and integrate it into their routines. This first principle calls to protect any domesticated technologies that smart products might affect. A smart product that is introduced should keep the original routines intact. For this, the traditional control must stay; lights can be controlled with a light switch, the TV can be controlled with a remote, indoor climate can be controlled with a thermostat and a door can be physically locked.
Adding new features
Smart products might introduce features which might collide with traditional functionality. In this case, the traditional functionality should be more easily discovered than the new features.
Give immediate control
Households are socially complex and routinely involve breakdowns, improvisations, compromises and conflicts (Davidoff et al., 2006). We are unpredictable, sometimes we’re sick or get home a bit late. This second principle calls to abandon automatic behavior once an exception is found. When a user tries to deal with an exception, they should immediately receive full control. Non-enthusiasts expect to receive full control when manually controlling their environments. When they turn the lights on, the lights are staying on.
Enthusiasts can adapt
When an enthusiast encounters a room that no longer responds automatically, they will wonder why and know how to fix this. They might be annoyed by this, but they can always fall back to their original, well domesticated, technology.
People are unpredictable, but this does not mean exceptions are impossible to solve. When a user deals with an exception, it can be used to learn from. By keeping track of these moments with immediate control, useful insights can be gained. Knowing if an exception happens often, with whom they happen, when they happen and what exactly happens are crucial pieces of information for improvement. Turning this information into actionable insights and making it accessible to the enthusiast, gives them the tools they need to improve an automation.
AI does something similar
This adaption is similar to smart products that have a self-learning system. With the creative problem-solving skills of an enthusiast, actionable insights likely rival or surpass the capabilities of a self-learning system.
Next chapter: Presenting slimmer dimmer