Threshold segmentation (TS) and slope-area (SA) approaches are employed according to the characteristics of small fluctuation of static activity signals and typical peaks and troughs of periodic-like ones. In this paper, a novel machine learning based segmentation scheme with a multi-probability threshold is proposed for HAR. Thus, effectively filtering out these activities has become a significant problem. However, the complete time serial signals not only contain different types of activities, but also include many transition and atypical ones. Most existing work for HAR is based on the manual labeling. In recent years, much research has been conducted on time series based human activity recognition (HAR) using wearable sensors. 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. In recent research, many works have been done regarding human activity recognition. 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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