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ALL MAKE GUIDES


  1. Security Zone
  2. Ultrasonic Drum
  3. Resistor Piano
  4. Walker Race
  5. Walker Dance
  6. Walker Detect
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  8. Rover Sensor Steering

  1. Lighthouse
  2. Frog Frenzy
  3. Game Controller
  4. Minecraft Controller
  5. Retro Racer

  1. Security Zone
  2. Ultrasonic Drum
  3. Resistor Piano
  4. Ther-Mood-Stat
  5. Color Coded
  6. Pulse

  1. Beam Break
  2. Car Race
  3. Motion Ball

  1. Solar House
  2. Soil Sensor
  3. Air Guitar

  1. Light Show
  2. Light Show Animation

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Base expedition

Mission 10 of 10

Base expedition: Mission 10 of 14

Pose Detect


Advanced

1 hour

Grades 3 - 8

MISSION OBJECTIVE

Learn about Machine Learning (AI), a subset of AI focusing on machines and very specific tasks.

Now that Piperbot and Pip have PAL up and running, they are ready to explore. Surprisingly, they've discovered that the ship's controls are damaged and can't be used on the flight deck. PAL, however, has a new idea and suggests that using a camera as a sensor, he could steer the ship based on the movements of Piperbot's head so that they could get going using the principles of Machine Learning.

View student interface at make.playpiper.com
MISSION CHARACTERS

Piperbot

Pip

PAL
MISSION MATERIALS

Computer with USB port and Chrome or Edge browser
Piper Make Base Station or Starter Kit

MISSION RESOURCES

Learning Goals

  1. Students will understand the basic principles of ML
  2. Students will gain a basic understanding of how ML works
  3. Students will understand using the camera as a sensor
  4. Students will build hardware that interacts with an existing ML model to track facial expressions

Learning Activities

The following sections will contain step by step instructions for ELA and ELD extensions directly related to this Mission. Adjust the directions to fit your ELA and ELD standards.

Grades 3-5

ELA Extension: Recognizing Piperbot Display the three character images below (Piperbot, Pip, and a creeper from Minecraft).


Ask students to share how they recognize the difference between the characters. They may say, “I’ve seen them before” or “It looks like a mouse.”

Discuss how artificial intelligence would recognize the difference between the characters - it would learn from seeing multiple images of the same character, and look for patterns. Ask students what are the patterns that tell us whether it is a robot, mouse, or creeper? Students should be able to describe particulars including colors, and the specific grid position of features like eyes, nose, mouth. How is pattern recognition the same and different from how humans perceive?

Now, have students consider how these images are very simple (64 pixels) compared to say, a self-driving car recognizing a pedestrian or a cyclist. When asking a computer to perceive photos or even video, the model will need an immense amount of examples to learn from.

ELD Extension: Pose Estimation Person Keep the code and build for the Pose Detect mission open. Have students click on the Camera Icon to view the pose estimation overlaid onto the webcam. Students should back up and stand in front of the camera and watch which body parts map to each dot. Students can also reference the drop down menu from the “camera (nose) pose (X position)” block to see which body parts pose detection works with.

Students should use these resources to draw and label a person with pose detection dots mapped onto each body part, including the list below. Students can draw the person in any pose. (This page has an example image for multiple poses: https://www.tensorflow.org/lite/examples/pose_estimation/overview)

  • Nose
  • Left Eye
  • Right Eye
  • Left Ear
  • Right Ear
  • Left Shoulder
  • Right Shoulder
  • Left Elbow
  • Right Elbow
  • Left Wrist
  • Right Wrist
  • Left Hip
  • Right Hip
  • Left Knee
  • Right Knee
  • Left Ankle
  • Right Ankle

Grades 6-8
ELA Extension: COCO Animals COCO, or Common Objects in Context, is a large-scale object detection, segmentation, and captioning dataset. Students can browse the Coco image dataset at https://cocodataset.org/#explore

Choose 1 animal and click “Search” to view images tagged with that animal.

Students should answer the following questions about their animal search.

  • How many images did your search produce? (This will display right under the search bar.)
  • How many different poses or angles did you see of your animal? Describe at least 3 of the different poses (ie, a horse as a logo on a sign and a horse pulling a cart.)
  • If you were going to teach what a zebra is, do you think it would be easier to teach a human or to teach an artificial intelligence? Why?
  • This dataset has a total of 2.5 million labeled instances in 328,000 images. Why do you think a machine learning model needs so many images to be trained effectively?

ELD Adaptation: COCO Animals COCO, or Common Objects in Context, is a large-scale object detection, segmentation, and captioning dataset. Students can browse the Coco image dataset at https://cocodataset.org/#explore

Choose 1 animal and click “Search” to view images tagged with that animal.

Students should answer the following questions about their animal search.

  • How many images did your search produce?
  • What are the features of your animal:
    • How many legs?
    • What colors?
    • How do you recognize this animal?
  • What tells the computer which part of the images is an animal? (The outlines, or bounding boxes)

Career Connections

Software Developer: Salary $127,260/yr
Electrical Engineer: Salary $127,260/yr
Data Scientist: Salary $103,500/yr

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Base EXPEDITION RESOURCES
  • Explanation of Pull Up and Pull Down
    What are Pull-Up and Pull-Down Resistors? Read this overview to learn more.
  • Google Teachable Machine
    A fast, easy way to create machine learning models for your sites, apps, and more – no expertise or coding required.
  • The Artificial Intelligence (AI) for K-12 initiative
    The initiative is developing national guidelines for AI education for K-12, an online, curated resource Directory to facilitate AI instruction, and a community of practitioners, researchers, resource and tool developers focused on the AI for K-12 audience.

Vocabulary Words

Circuit A conductive path for flow of current or electricity

Power The current or flow of electric charge and voltage

Microcontroller An integrated circuit that contains a microprocessor along with memory and associated circuits

Loops A sequence of instruction s that is continually repeated until a certain condition is reached

Variables A value that can change, depending on conditions or on information passed to the program

Input Device A hardware device that sends data to a computer, allowing interaction and control

Output Device A piece of hardware which converts information into a human-perceptible form

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