Skip to main content

Word Adventure, gather letters to complete the word!

This is an interactive installation that aims to combine learning words and fun in devising strategies to gather letters.


Collect letters to complete the word

The letters are dangling at various locations, and only the yellow colored characters can collect them. But these characters cannot jump that high, so players need to put blocks as stepping stones to help these cute yellow fellas to reach the letters. The blocks are generated by touching the screen (wall) and can be removed by touching it again.

System overview

The system is simply a projector and a laser range finder to detect touches on the wall. You may realize the black object in the photo above at the top of the wall. This is the laser range finder and for this project I used Hokuyo's UST10-LX. Content is developed with Unity.

How the touch detection works

Readings from the sensor

This image is the visualization of the data points detected. With the sensor placed on top of the wall looking down, if there are no hands touching the wall the laser beams will reach the floor. This will result in a rectangular shape with the floor points aligning as a straight line. However, if someone touches the wall, its position will be detected as shown in the above image. So to detect the touches we just have to first define the rectangular touch area, and all points that are within this area are touch (hand) points.

Limitations

There are some limitations in accuracy and detection rate. If there are two hands (touch points) that overlap each other from the sensor's point of view, the one on the outer side will not be detected.

Undetected touch point

As shown in the image,  the bottom touch point will not be detected because it is hidden by the other one above it. The black line represents the laser beam that sweeps the wall for obstacles (hand/touch points) and we can see it cannot reach the bottom point.



Comments

Popular posts from this blog

Installing a custom ROM on Android (on the GT-N8013)

It's been a while since my last entry and since it is a new start in 2019, I thought I'd write something about "gone with the old and in with the new". I've had my Samsung Galaxy Note 10.1 (pnotewifi) since 2014, and it's one of the early Galaxy Note tablet series. It has served me well all this years but now it just sits there collecting dust. My old Samsung GT-N8013 I've known a long time about custom Android ROMs like CyanogenMod but has never had the motivation to try them out, until now ! Overview of the process For beginners like me, I didn't have an understanding of the installation process and so it looked complicated and it was one of the reasons I was put off in trying the custom ROM. I just want to say, it's not complicated at all!   Basically you will need to Prepare an SD card and install Android SDK (you need adb ). Install a custom boot loader ( TWRP is the de facto tool at the moment). Use adb to copy custom...

Using FCM with the new HTTP v1 API and NodeJS

When trying to send FCM notifications I found out that Google has changed their API specifications. The legacy API still works but if you want to use the latest v1 API you need to make several changes. The list of changes is listed on their site so I won't be repeating them again but I'll just mention some of the things that caused some trial and error on my project. The official guide from Google is here : Official Migration Guide to v1 . The request must have a Body with the JSON containing the message data. Most importantly it needs to have "message" field which must contain the target of the notification. Usually this is a Topic, or Device IDs. Since my previous project was using GAS, my request had a field called "payload" instead of "body". Using the request from my previous project, my request in Node JS was as follows: request ({ url: 'https://fcm.googleapis.com/v1/projects/safe-door-278108/messages:send' , method: ...

Object detection with Google Colab and Tensorflow

This is just a memo of the challenges I faced when running a model training on Google Colab, while following a great tutorial here . Mind the versions Tensorflow is currently at version 2.2.0 but most tutorials are still using the contrib package, and there is no known easy way to update the code to remove dependency on contrib. So my best bet is to downgrade the tensorflow version to 1.x. Since Google Colab only gives the options of either 1.x or 2.x and we cannot specify the exact version, I ended up with version 1.15.2. Even with the command :  %tensorflow_version  1.15.0 I ended up with : 1.15.2 Another pitfall was the version of numpy. Installing numpy gives us the version 1.18.3 but for some reason this generates the error : TypeError: 'numpy.float64' object cannot be interpreted as an integer Downgrading numpy to version 1.17.4 solved this for me. It seems we don't need ngrok for tensorboard With the command :  %load_ext tensorboard W...