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People spend 90% of their time being indoors, and indoor comfort has been proved a significant factor influencing human physical and mental health. So it is considered more and more important for potential tenants during their house finding.

However, Most people’s initial understanding of the comfort level comes from mere descriptions from the landlord or real estate agencies. People do not have the time and opportunity to understand what it really feels like to live in a property before moving in.

Moreover, there’s no easy way at the moment to know how the building perform at different time during the day or under different weather conditions, this is not a problem that can be solved by just paying more site visits.

In summary, it’s always difficult for potential tenants to access to the accurate information on the indoor comfort during their house finding, which might lead them to signing a lease that is not meeting their demands for desirable comfort level or not commensurate with the actual price .

In summary, it’s always difficult for potential tenants to access to the accurate information on the indoor comfort during their house finding, which might lead them to signing a lease that is not meeting their demands for desirable comfort level or not commensurate with the actual price .

We want to change this situation by introducing QI *- An AR application that conducts fast energy simulation and visualizes the results as temporal and spatial information overlaying on the real world environment, assisting potential tenants to make better decisions during house finding.

*In traditional Asian culture, QI (or chi) is believed to be a vital force forming part of any living entity. Qi translates figuratively as "material energy", "life force", or "energy flow".


User Analysis

The user we are targeting is the potential tenants who are looking for comfortable living space. To better understand their pains and gains, we conducted user empathy and journey study.


Empathy Map


Journey Map


We propose an AR application in which we use location- aware technology and connect to the history weather database geographically. Once enter the application, users can scan the space Using the camera of their device, and generate a 3d model of the space. Based on the geometry, fast energy simulation will start running at backstage, the simulation result will be presented to user in form of augmented reality.

User Flow

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Home Page

Compare Mode

Community Mode

Scan Mode

Home Page


Once the user logs in, the app will have access to the Location Corresponding weather and geographic data at where he or she is. Then the user will be guided to the homepage, where he or she can access three modes through buttons at the bottom.

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Scan Mode


Following the instruction, the user will scan the room, and get the basic geometry of the room, and position of windows for later simulation and visualization

User can then choose one of the three modules of visualization types(ventilation, lighting, heat). Before each module, we provide a short ,easy to understand tutorial on how to read the visualization results.

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Module1: Ventilation

After getting user location and corresponding weather data, a virtual wind tunnel will start and constantly blow wind from the prevailing wind direction, we use flow lines to describe the feature of the wind field, such as wind path and turbulence area. The color indicates the speed of the particle along the path. User can change the prevailing wind direction by changing the month and the flow lines will change as well.

Module2: Lighting

In the default display mode, the dynamic change of the sunlight will be overlaid onto the real space, again, with the sidebar, you can access to any hourly data throughout the year. Here we can see it’s fairly dark in the morning and lots of direct sunlight in the afternoon, which may cause overheating in the summer afternoon.  You can toggle on the Illuminance mode, where you can inspect exact surface illuminance. There’s also an overlay of potential glare or overheating area to give you more in depth information about the lighting environment.

Module3: Heat

You can see how the Mean Radiant Temperature changes with time. This can give you metric at any given time how cold or hot you will feel in this space, especially in the extreme weather days. With this information, you can value in advance how warm the room will be in winter even you can only visit the house in summer. 

Module4: Furniture

Another module is about the furniture layout, we have a standard furniture library and you can inspect if the furniture fit the space in size and also overlay the furniture with physics data we have just shown and make some decision on where to put the furniture gives you best comfort.

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Comparison Mode

If you need to compare multiple offers, we have a comparison mode where you can layout the geometries as scaled models with saved simulation results superimposed on them. You can also tune the data and time here to see the changing patterns.

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Community Mode

The third core feature is community, where users can upload or download scanned model

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Evaluation and Reflection

This idea was initialized as a project for the class 'Design From Within--How New Immersive Technologies Create Value'. We first diagnosed the problem from our own life experience--being deceived by the landlord about the poor performance of the house. We pitched to our instructor the idea of visualizing the indoor climate data to ordinary people and assisting them to make better decisions when choosing a house to live in. 

First, we discussed the idea mainly with the instructor of the class and defined the problem, identified our target users and value proposition.  Then we conducted the user survey and market research to polish a more detailed idea. After the ideation, based on our own background, we reviewed some of the existing applications that can do part of the job we envisioned and analyzed the feasibility of implementation into our product, then we sketched and designed the mockup solution.

So far, we've presented our mockup solution to our potential customers,   people who've been working in AR or building energy field and people who are in the business background. We concluded the two main suggestions as follows,

Firstly, the sense of comfort of a space can vary from individual to individual. It might be necessary to add a module where users can set up their personal profile and environmental preferences. Then the product can recommend suitable houses or apartments based on their preferences. 

Secondly, the current visualization result might be too technical oriented, we should find better ways to tell non-professional users how to read and interpret the results.

Thirdly, think about who is going to pay for this app and business plan in detail.