Project 2 plan

Revised : 

Seven Scenarios-Informed Research Question:

How does AR-supported lighting adjustment influence users’ ability to interpret and manipulate lighting environments for personal goals?

In class activity:

A scenario-based task would be given to participants. Participants will use both their mobile phone and the AR tool to adjust the lighting to make the room ‘feel cozy for guests.’

1. Participants will take a photo of the space and use the photo editing tool to adjust the temperature. (reference attached)

2. Participants will use the AR tool (color temperature sliders or lighting presets) to control lighting.

Evaluation Questions:

• How do participants define and achieve the subjective goal of “cozy” differently with each tool?

• How effectively does the AR tool support participants in achieving a desired atmosphere (e.g., 'cozy')?

• What are the differences in user experience between mobile photo editing and AR-based lighting adjustment?

Evaluation Metrics

Quantitative

- Time it takes to complete each task

- Self-rated confidence score (1–10) for each method

- Frustration level, ease of use, clarity of UI and overall satisfaction for each method(1-10)

- How easy it was to figure out how to use each tool at first (1-10)

Qualitative

- Description of “cozy lighting”

- Visual comparison of final photo edit vs. AR-adjusted space

- Likes and dislikes of each method

- Discussion on how lighting adjustment decisions are currently made without the tool

*Feedback 

• Before activity headset setting should be 'default'

• capture differences between the photo-edited image and AR snapshot objectively, maybe by “measuring” the warmth or lux

• have participants self-report is how much of a specific color/filter they added into their AR space

•  get the user's perceived preferences before they start the activity by showing some photos in the form and they select what they imagine "cozy" looks like. This might help create some baseline with how people set up their lights.
  
  

Previous Plan : 

1. Estimating light intensity in passthrough mode and displaying UI in the environment. (Investigate for a week and if doesn't work, move on)

2. Control lighting in passthrough mode by using lighting spheres and display UI of changed information.
(If step1 didn't work,  work on different types of lightings and refining visualization so that certain areas in passthrough would be affected)

In class activity : Either capturing image in passthrough and making it to a square, or comparing project 1 with project 2.

Project Evaluation

• The proposed project clearly identifies deliverable additions to our VR Software Wiki : 5

• involves passthrough or “augmented” in VR : 5

• The proposed project involves large scientific data visualization along the lines of the "Scientific Data" wiki page and identifies the specific data type and software that it will use : 4

• The proposed project has a realistic schedule with explicit and measurable milestones at least each week and mostly every class : 5

• The proposed project explicitly evaluates VR software, preferably in comparison to related software : 5

• The proposed project includes an in-class activity, which can be formative (early in the project) or evaluative (later in the project) : 5

• The proposed project has resources available with sufficient documentation : 4

Project Evaluation

I can use...

1. Understanding Environments and Work Practices (UWP) 

• I can research and analyze how lighting decisions are currently made in different environments (e.g., offices, classrooms, homes). 

• I can talk to RISD interior architecture students / architecture student / students who moved recently to understand their pain points and expectations when making decisions on lighting.

2. Evaluating User Performance (UP)

• I can give a task to users ( for instance to aim for a certain lux number) and measure task efficiency and accuracy in adjusting lighting in passthrough environment. Time taken to achieve a target light intensity could be the metric.

3. Evaluating User Experience (UE)

• User experience will be qualitatively assessed through surveys and discussions. (Similar to project 1 surveys)

• I will collect feedback on ease of use, clarity of UI, and overall satisfaction.

4. Evaluating Visualization Algorithms (VA) 

• The accuracy and computational efficiency of light intensity detection could be tested.

• How precisely the tool detects lighting conditions would be the key performance metric.