IS480 Team wiki: 2012T1 M.O.O.T/Technical Applications
User Testing 1 | User Testing 2 | Development Technologies | Hardware | Primary Research | Secondary Research |
Contents
User Testing 1
Our UT 1 took place on 24 September 2012 from 12pm to 4.30pm.
Objectives
- Test the accuracy of gender recognition in a real live setting
- Gather users’ opinions about photo taking using augmented reality
Setup
Location: The T-junction at the school’s basement is selected because it
- Emulates a real live mall setting
- Has a comparatively high human traffic so as to garner as much feedback as possible
- Has 7m * 6m (~452 sq ft) of floor space
- Has a plain wall background on one side of the booth (as Kinect sensor performs best with plain background)
- Has 1 power outlet to provide power for laptop and equipment
The setup at the T-junction will follow as stated here while the overview of setup layout will be as shown:
Scope completed
- Gender Recognition through:
1. Detection of height
2. Detection of shoulder width & center of moment - AForge learning algorithm
- Prototype of photo taking
1. Countdown timer (to signal the start of photo taking)
2. Snapping of photo
Testers
We have a total of 70 testers – 31 females and 39 males. As one of our focus is to check the accuracy of our gender recognition algorithm, we picked our testers in the following way:
Female | Male | |
Height | 14 with height ≤ 1.6m 14 with height > 1.6m but < 1.7m 3 with height ≥ 1.7m |
13 with height ≤ 1.7m 19 with height > 1.7m, but < 1.8m 7 with height ≥ 1.8m |
Center of moment (COM) | We found 2 pairs of females and males that have similar height and similar body proportions | Same as left side |
Standard of Procedure (SOP)
1. Let tester use AlterSense by himself/herself
2. Record down the details of each tester into [Gender Recognition Metrics]
3. Record a short video of tester using Kinect Studio
4. Let tester fill up a survey form
Results of survey form
Feedback on gender recognition
Based on these results, we decided to have neutral promotions that are suitable for both gender groups for shoppers that we don’t have full confidence in detecting their gender since 37% of the people surveyed have indicated that they would be offended if they were recognized of the wrong gender.
As 58% of the people surveyed indicated that they are more likely to purchase an item from a targeted promotion that is specific to their gender group as opposed to that from a neutral promotion suitable for both gender groups, this justifies our main objective of having targeted advertising to enhance shopper’s experience.
Feedback on photo taking
All of our testers had no difficulty taking a photo with the majority finding it very easy to take a photo with AlterSense.
Around 40% of the testers found that the countdown timer which was used to signal the start of photo taking, was confusing and not intuitive at all. Among them, some did not notice the presence of the countdown timer. Thus we have decided to implement a speech bubble with words such as “Let’s take a photo” to make the signal of starting photo taking more intuitive.
Results of accuracy of gender recognition algorithm
The gender recognition is pretty accurate at a rate of 87.14% after testing 70 people. Some of the reasons of the inaccurate detection of gender were due to the inability of Kinect to handle reflective items such as polished shoes or shiny pants. This caused the legs of the tester to be cut short as Kinect was not able to detect the full length of the legs.
Wearing skirt
Hair