Unit 1A: Basic Circuits

Objective

Learn to use a support vector machine (SVM)for classifying multi-dimensional sensor data, among a group of pre-defined classes of feature vectors. Specificlly, learn to utilize SVM to recognize the orientation of a mobile phone, based on its accelerometer sensors and a series of pre-defined orientations.

Steps and observations

Begin by setting up a mobile phone to send accelerometer data to a computer using UDP over a wireless network.

1. Click on "clear" to clear the SVM model
2. Click on "learn" to enter training mode
3. Hold your mobile phone in a particular orientation and click on the first square 10-20 times. Each time you click, a 3-dimensional "feature vector" made up of the x, y and z acceleration values is sent into the SVM as an example of class 1.
4. Hold your mobile phone in a different orientation and click on the second square 10-20 times. Each time you click, a 3-dimensional "feature vector" made up of the x, y and z acceleration values is sent into the SVM as an example of class 2.
5. Repeate steps 3-4 for several more poses
6. Click on "train" to create the SVM model
7. Click on "map" to enter mapping mode
8. Move your phone among different trained orientations, compare with the the classification results show in the interger box under "ml.svm"
9. Turn ON "probs" to see the likelyhood of each class as a continuous variable

Comments

• How else can this technique be used?
• How many orientations can you recognize with this technique?
• How can you help the SVM be more accurate, but adding additional sensor information to the feature vectors?

Other Files

1. orientation-classification.pd