Abstract-Smartphones are widely used today, and it becomes possible to detect the user's environmental changes by using the smartphone sensors, as demonstrated in this paper where we propose a method to identify human activities with reasonably high accuracy by using smartphone sensor data. First, the raw smartphone sensor data are collected from two categories of human activity: motion-based, e.g., walking and running; and phone movement-based, e.g., left-right, up-down, clockwise and counterclockwise movement. Firstly, two types of features extraction are designed from the raw sensor data, and activity recognition is analyzed using machine learning classification models based on these features. Secondly, the activity recognition performance is analyzed through the Convolutional Neural Network (CNN) model using only the raw data. Our experiments show substantial improvement in the result with the addition of features and the use of CNN model based on smartphone sensor data with judicious learning techniques and good feature designs.
In recent years, there has been an increase in the association of technology in sports and live sports broadcasting networks. From score updates, broadcasting commercials, assisting referees for decision making, and minimizing errors, the adoption of technology has been used for fair play and improve results. This has been possible with the advancement in video analysis, classification techniques, and the availability of resources. This paper introduces a new labelled video dataset collected from a live basketball game broadcasted on live TV to determine the type of basket scored in the basketball game. Among different shots, the points the player can score are basically of three types: 3 points, 2 points, which depends on the range of shots taken and 1 point which is the free shots taken after a foul. This dataset consists of labelled video clips collected from the live broadcast of the game from the broadcasting medium to classify different scoring activities. This paper also gives the preliminary analysis of the dataset for different class labels using 3D ConvNet and two-stream 3D ConvNet methods to show the complexity of the dataset.
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