Sensor Technologies by Michael J. McGrath & Cliodhna Ní Scanaill

Author:Michael J. McGrath & Cliodhna Ní Scanaill
Language: eng
Format: epub, pdf
ISBN: 9781430260134
Publisher: Apress

Figure 7-3. Example of a possible data economy for exploring allergies

In this scenario, the end user benefits twice—she finds a better jogging route and works out the cause of her allergy problem. The ability for machines to find patterns and synthesize them supports end users in making their own decisions and developing their own hypothesis. It stops short of presuming to know what the answer is; instead, the problem the user is trying to resolve shifts over time, and her use of biosensors and other data shifts with that evolution. Within this scenario, different designers might make different decisions about how much automation is useful. Some end users will want to see more of the granular data, while other end users might want a system to alert them to what other people have also tried to collect. No air quality monitoring system manufacturer will be able to anticipate all the uses to which that data could be put, but the ability to compose data in ways designers cannot anticipate makes it possible for end users as well as companies to benefit from data exhaust—the flow does not go one way. With improved data security technologies that would allow people better control over their data exhaust, it is possible that the balance could be evened up even more. Another difference is that the institutional beneficiaries of the data are more heterogeneous, which opens up the possibility for additional solutions should it prove impossible to simply avoid poor air quality by changing one’s route through the city. It is entirely possible, of course, that the various actors involved in this scenario may have poor relations or conflicting incentives and might not want to see data exchanged in this way. But by emphasizing exploration, we open up the possibility to create alignment or otherwise manage conflicting incentives and decrease the probability of the data simply falling into disuse.

Once we shift from thinking of technologies as machines that tell us what we should do to machines that tell us what could be going on, the role of pleasure and fun changes. In the mobile health space, there are many attempts at “gamification,” also noted in the foreword and Chapters 10 and 11 of this book. In gamification, developers use Skinneresque reward systems to entice people into doing what has been pre-ordained to be good for them. Some designers are having success with these techniques, but people are more than Pavlov’s drooling dogs hoping for the next treat. Already in this space we see plenty of examples of people adapting the sensors currently on the market for fun and creative endeavors. Indeed, I interviewed a man who wore a continuous heart monitor out of sheer curiosity and happened to get into a car accident while wearing it. The data stream from that moment holds a particularly poignant memory for him. The monitor is hardly there for diagnostic or disease management reasons, and “pleasure” would not be the best word to describe his relationship to that data, but it nevertheless has personal significance for him.

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