Data mining is a process of identifying valid information from the large databases. There are many different tasks in data mining such as classification, clustering, prediction, time series analysis, sequence pattern mining, etc.Activityrecognition aims to identify the actions and goals of one or more agents from a series of observations on the agent's actions and the environmental conditions. The activity recognition, having considerably matured, so has the number of challenges in designing, implementing, and evaluating activity recognition systems in exiting methodologies. The proposed research focuses on activity recognition using sensors dataset. The proposed research challenges that Human Activity Recognition shares with general pattern recognition and identify those challenges that are specific to Human Activity Recognition.
The Human Activity Recognition (HAR) refers to the task of measuring the physical activity of a person via the use of objective technology. This task is extremely challenging owing to the complexity and diversity of humans. The concept of an activity recognition chain as a general-purpose framework for designing and evaluating activity recognition systems. The proposed research comprises components for data acquisition and preprocessing, data segmentation, feature extraction and selection, training and classification, decision fusion, and performance evaluation. The proposed research concludes with the sensor data example problem of recognizing different hand gestures from inertial sensors attached to the upper and lower arm. The proposed research can be implemented for this specific activity recognition problem and demonstrate how different implementations compare and how they impact overall recognition performance.
