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“Parameters and calibration of a low-g 3-axis accelerometer.” 2014. STMicroelectronics, AN4508 Application note. Use a ThingSpeak™ write block to publish classified activities and acceleration data from your device to the Internet of Things. Also, you can add another output block for audio feedback to the output subsystem using Audio Toolbox™. To improve the model, you can consider using additional sensors and updating the calibration matrix. The accuracy of the model can be different from the accuracy of the test data set ( testaccuracy), depending on the device. If you want to place your device in a different location or orientation, then collect the data in your own way and use your data to train the classification model. To ensure the accuracy of the model, you need to place your device in the same way as described for collecting the training data. The model displays the classified activity accordingly. Run the model on your device, place the device in the same way as described earlier for collecting the training data, and try the five activities.
Human activity detection matlab code for android#
To deploy the Simulink model to your device, follow the steps in Run Model on Android Devices (Simulink Support Package for Android Devices) or Run Model on Apple iOS Devices (Simulink Support Package for Apple iOS Devices). The Convert to RGB block decomposes the selected image into separate RGB vectors and passes the image to the Activity Display block. The Video Output subsystem uses a multiport switch block to choose the corresponding user activity image data to display on the device. The Predicted Activity block displays the classified user activity values on the device. The output is an integer between 1 and 5, corresponding to Sitting, Standing, Walking, Running, and Dancing, respectively. The predictActivity block is a MATLAB Function block that loads the trained model from the EnsembleModel.mat file by using loadLearnerForCoder and classifies the user activity using the extracted features. This function block uses DSP System Toolbox™ and Signal Processing Toolbox™. The extractFeatures block is a MATLAB Function block that extracts 60 features from a buffered frame of 32 accelerometer samples. Each Buffer block receives an input sample every 0.1 second and outputs a buffered frame including 32 samples every 2 seconds. After collecting 20 samples, each Buffer block joins the 20 samples with 12 samples from the previous frame and passes the total 32 samples to the extractFeatures block. 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.Įach 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. If you click the button located in the upper-right section of this page and open this example in MATLAB, then MATLAB 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.
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The Accelerometer block receives raw acceleration data from accelerometer sensors on the device. Use cvpartition to specify a 10% holdout for the test set. 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. 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.
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The Sensor HAR (human activity recognition) App was used to create the humanactivity data set. Actid - Response vector containing the activity IDs in integers: 1, 2, 3, 4, and 5 representing Sitting, Standing, Walking, Running, and Dancing, respectivelyĪctnames - Activity names corresponding to the integer activity IDsįeat - Feature matrix of 60 features for 24,075 observations