Know everything about your building, now and forever.
IoT sensors are becoming increasingly available for monitoring the condition of a building. However, these sensors often don’t have an easy way of viewing and managing all the data they provide, in a manner that is actually useful for building managers.
The following design was researched and mocked up as part of a 4 hour design challenge.
Based on the brief and design research an app was mocked up named ‘Sensorium’, with key features:
View every sensor
Compare over time
Receive critical alerts
10 minutes – Timeline and Research Plan
25 minutes – Raw Design Research
10 minutes – Research Synthesis
15 minutes – User Journey and Key Screen Selection
180 minutes – High Fidelity Design Screens
45 minutes – Final Documentation (Not included as part of 4 hour Design Challenge time)
User research is always important, regardless of the timeframe of a design problem. Participants were contacted who had similar responsibilities and workflows to that of a Building Manager overseeing a building with multiple sensors. These four participants were either observed or quickly interviewed, with particular note to workflows, pain points, and opportunities.
👮🏻♂️ Office Security Guard
👨🏼🔧 Small Office Manager
💁🏽♀️ Floor Manager
👩🏼💻 I.T. IoT Manager
The findings from this research was synthesized into the following key takeaways:
“I care about Critical Alerts” – There’s a desire to be notified of mission critical alerts, with the location of the alert often being the priority.
“Meet me where I am” – A participant’s primary touchpoint isn’t always a desktop screen, it may be a phone, wearable, or through Slack. Labels are also often sufficiently intuitive, and geospatial visualization is unnecessary.
“My experiments could benefit from Sensor Comparisons” – Comparison of sensors can sometimes be desirable, especially when running experiments to test social effects, or evaluating the accuracy of sensors by their location.
“Trend Analysis could help me evaluate Sensor Degradation” – Long-Term (e.g. yearly) trend analysis can be useful for the long-term monitoring of sensor accuracy.
In highlighting out key takeaways in the research, we can start creating potential user stories and journeys that represent common tasks. Here is one such story:
Sam gets a buzz on her watch, it looks like there’s “High CO2 in Garage B”. As she starts walking to Garage B, she taps the watch and sees a value of over 5,000 parts per million which is incredibly high and can cause health defects after prolonged exposure. Upon entering the garage, she notices an old diesel car that has is on but isn’t moving, with someone browsing their phone inside. She knocks on the window and signals for the person to turn off their engine.
Back at her desk, Sam opens her laptop, seeing an overview of all the sensors in the building. She zooms in on the CO2 levels of the garages over time and notices that there were some other smaller peaks that were just below the alert threshold. Curious as to what these peaks are, she sets the threshold a bit lower so she’ll be notified the next time they occur.
After a few weeks, Sam has noticed that many visitors to the building have a tendency to leave their cars running when they’re about to leave. It seems they’re struggling to slowly get a signal to redirect them home from the underground visitor parking area. Sam decided to do an experiment, moving the visitor parking area of Garage B to an area that has reception, but is a bit of a longer walk for visitors.
After a month, Sam compares the difference in CO2 levels between Garage A and Garage B and is happy to note that Garage B saw a significant drop in CO2 levels, as well as occupancy levels, since people weren’t spending as much time trying to get a signal to find their way home. She brought up her findings with others, and is now implementing the additional sensors and changes to visitor parking locations in all garages.
From the user journeys, some key screens were selected. Lo-fi designs were sketched out of these screens and then quickly turned into high fidelity visuals.