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WEKA JAR JAVA HOW TO
WEKA JAR JAVA ANDROID
WEKA JAR JAVA CODE

When you collect and label activities - like walking, running - make sure that you walk, run, etc. You will see what I mean when you get to that part. Make sure you only cut and paste the WekaClassifier class and not all the other information provided by WEKA.

Cut and paste the class WekaClassifier into the WekaClassifier.java file in your MyRuns5 project.

Create a decision tree J48 classifier from the features.arff data.

This will start WEKA assuming you have installed it correctly.

Double click on the features.arff file.

WEKA JAR JAVA ANDROID

Use the file explorer in Android Device Monitor to upload the features.arff file and store it in any folder on your computer (you can use any text editor to view the data - as you see it is all the samples and labels, a label is for example when you tell the collector this is walking).

Your data will be saved in a file on your phone called features.arff - it's a critic name hey.

Use the collector to collect your data - standing, walking, running (you can do other classes to - by the way the term classes also means activity, the running class, walking class - we simply say classes to mean, in a general sense, all activities activities = classes.

Import the myrunsdatacollector.zip into Android Studio and load it on to your phone.

The training phase boils down to the following steps at a high level:

WEKA JAR JAVA CODE

The training phase is illustrated in the figure below - don't panic: we do not expect you to write low-level signal processing functions such as compute the FFT - we give out code to do that.

WEKA JAR JAVA HOW TO

I will show how to collect activity data using MyRuns Data Collector and teach you how to play with Weka GUI to train your own classifier. In what follows, we only discuss the training phase where we collect data and create a classification model. calling the WekaClassifier inputting the feature vector - this is called the classification phase and produces an inference (e.g.adding the maximum value of the magnitude across all 64 individual magnitudes to the feature vector and.segmenting features into batches of 64 features in a buffer - this is called the feature vector.computing the FFT coefficient of the magnitude - this is called a feature.computing the magnitude from a single x, y, z reading.reading the x, y, z sensor data from the SensorManager.Other parts of the pipeline are as follows (include the WekaClassifier): Second, the classification or inference phase where we embed the WekaClassifier produced by WEKA into our MyRuns5 project in a file called WekaClassifier.java - this forms one component of the classification pipeline that runs as part of your application.Technically, this technique is called "supervised learning" in machine learning (ML) parlance. First, the training phase where we collect data using the MyRuns data collector (which you are given) and label the data (e.g., walking) and then feed the resulting sensor data into WEKA(Waikato Environment for Knowledge Analysis - a very cool machine learning tool) which, in turn produces a classifier (called the WekaClassifier java class in our MyRuns5) that we embedded in our MyRuns5 to form a component in what is called the activity recognition "classification pipeline.In this lecture, we will discuss the training phase of activity recognition the complete machine learning task of building a classification system that can automatically infer your activity (e.g., standing, walking, running) breaks down into two sequential phases: