SVM accelerometer feature selection gyroscope human activity recognition (HAR) machine learning sensor. Our proposed system works efficiently with limited hardware resource and provides satisfactory activity identification. We validated our model with a benchmark dataset. Features are then fed to a multiclass support vector machine (SVM) to create nonlinear classifiers by adopting the kernel trick for training and testing purpose. The process uses a sequential floating forward search (SFFS) to extract desired features for better activity recognition. This research has proposed a hybrid method feature selection process, which includes a filter and wrapper method. Including all feature vectors create a phenomenon known as 'curse of dimensionality'. However, all the vectors are not contributing equally for identification process. Sensor data of smartphone produces high dimensional feature vectors for identifying human activities. The small, low-power Bosch-BMI160 is a low noise 16-bit IMU designed by Bosch used as an accelerometer and gyroscope at 0.002m/s 2 and 0.030/rad. They run the Oxygen OS 10.3.4 android operating system. In recent research, many works have been done regarding human activity recognition. It consists of the accelerometer, gyroscope, and magnetometer sensors required to recognize physical activities. Smartphones incorporated with varieties of motion sensors like accelerometers and gyroscopes are widely used inertial sensors that can identify different physical conditions of human. Human activity recognition (HAR) techniques are playing a significant role in monitoring the daily activities of human life such as elderly care, investigation activities, healthcare, sports, and smart homes.
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