HUMAN ACTIVITY DETECTION MATLAB CODE FOR ANDROID
HUMAN ACTIVITY DETECTION MATLAB CODE SOFTWARE
HUMAN ACTIVITY DETECTION MATLAB CODE CODE

Consider people on crutches, people in wheelchairs, people on unicycles, bicycles, or tricycles. Gait recognition is not sufficient for your purpose. Human Infrastructure & Human Activity Detection.

HUMAN ACTIVITY DETECTION MATLAB CODE CODE

Code for displaying data through MATLAB 11. Detecting Human Activity using Acoustic, Seismic. Kinect and MATLAB, real-time human detection.

Each of the Buffer blocks, X Buffer, Y Buffer, and Z Buffer, buffers 32 samples of an accelerometer axis with 12 samples of overlap between buffered frames.Abstract - H uman detection, tracking and activity recognition is an important area of.

The display blocks Acc X, Acc Y, and Acc Z are connected to the calibrate block and display calibrated data points for each axis on the device.

If you click the Open Live Script button and open this example in MATLABВ®, then MATLABВ® opens the example folder that includes these files. This block uses the calibration matrix in the slexHARAndroidCalibrationMatrix.mat file or the slexHARiOSCalibrationMatrix.mat file.

The calibrate block is a MATLAB Function block that calibrates the raw acceleration data.

The Accelerometer block receives raw acceleration data from accelerometer sensors on the device.

HUMAN ACTIVITY DETECTION MATLAB CODE FOR ANDROID

Type slexHARAndroidExample to open the Simulink model for Android deployment. If you click the Open Live Script button and open this example in MATLABВ®, then MATLABВ® opens the example folder that includes these calibration matrix files. Note that the Simulink model requires the EnsembleModel.mat file and the calibration matrix file slexHARAndroidCalibrationMatrix.mat or slexHARiOSCalibrationMatrix.mat. Deploy Simulink Model to Device Now that you have prepared a classification model, you can open the Simulink model, depending on which type of smartphone you have, and deploy the model to your device. SaveCompactModel(classificationEnsemble, 'EnsembleModel.mat') The function block predictActivity in the Simulink models loads the trained model by using and uses the trained model to classify new data. ClassificationEnsemble = trainedModel.ClassificationEnsemble Train Boosted Tree Ensemble at Command Line Alternatively, you can train the same classification model at the command line. Extract the trained model from the structure. The field ClassificationEnsemble of trainedModel contains the compact model. The structure trainedModel appears in the MATLAB Workspace. On the Classification Learner tab, in the Export section, click Export Model, and then select Export Compact Model.

Classification Learner detects the predictors and the response from the table. In the New Session dialog box, click the arrow for Workspace Variable, and then select the table tTrain. On the Classification Learner tab, in the File section, click New Session and select From Workspace. Then, under Machine Learning, click Classification Learner. Alternatively, click the Apps tab, and click the arrow at the right of the Apps section to open the gallery. To open the Classification Learner app, enter classificationLearner at the command line. = Train Boosted Tree Ensemble Using Classification Learner App Train a classification model by using the Classification Learner app. Use to specify a 10% holdout for the test set. Prepare Data This example uses 90% of the observations to train a model that classifies the five types of human activities and 10% of the observations to validate the trained model. A Matlab code is written to recognize human actions namely 'walking', 'jogging','running', 'boxing','hand waving', and 'hand clapping' using Spatio Temporal Interest Points (STIP) and classify the same using a KNN classifier. Moreover, MATLAB codes are so simple and easy to understand. All actions were constant and manually chosen, another where number of topics for. Human action recognition is an important topic of computer vision research and. The Simulink models described later also use the raw acceleration data and include blocks for calibration and feature extraction. For details about the calibration and feature extraction, see and, respectively.

HUMAN ACTIVITY DETECTION MATLAB CODE SOFTWARE

The software then calibrated the measured raw data accordingly and extracted the 60 features from the calibrated data. When measuring the raw acceleration data with this app, a person placed a smartphone in a pocket so that the smartphone was upside down and the screen faced toward the person.

featlabels - Labels of the 60 features The Sensor HAR (human activity recognition) App was used to create the humanactivity data set.feat - Feature matrix of 60 features for 24,075 observations.actnames - Activity names corresponding to the integer activity IDs.actid - Response vector containing the activity IDs in integers: 1, 2, 3, 4, and 5 representing Sitting, Standing, Walking, Running, and Dancing, respectively.

Human Activity Detection Matlab Code