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. 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.

The Sensor HAR (human activity recognition) App was used to create the humanactivity data set. The data set contains the following variables:Īctid - 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 Each observation has 60 features extracted from acceleration data measured by smartphone accelerometer sensors. The humanactivity data set contains 24,075 observations of five different physical human activities: Sitting, Standing, Walking, Running, and Dancing. Simulink support packages are required for the Simulink models in this example.ĭownload and Install Simulink Support Package for Android Devices (required for Android deployment) Sims 2 uncensor patch.ĭownload and Install Simulink Support Package for Apple iOS Devices (required for iOS deployment) After you install the required support package for a target device, train the classification model and deploy the Simulink model to the device. The example provides two Simulink models that are ready for deployment to an Android device and an iOS device. This example shows how to prepare a Simulink® model that classifies human activity based on smartphone sensor signals for code generation and smartphone deployment. In Proceedings of the IEEE international conference on computer vision, pages 2720–2727, 2013.

Abnormal event detection at 150 fps in matlab. Generate the pose trajectories, followed by training and testing. The problem of human abnormal activity detection has been. Earlier research used stereo cameras to estimate human poses or perform human tracking 3, 4, 15. In recent years, there is a body of research on the problem of human parts detection, pose estimation and tracking from 3D data. Used in human environment and facilitate the research in human detection, tracking and activity analysis. Human Activity Recognition Simulink Model for Smartphone Deployment

Human activity detection matlab code