Vision Based Activity Recognition Using Machine Learning and Deep Learning Architecture

Human Activity recognition, with wide application in fields like video surveillance, sports, human interaction, elderly care has shown great influence in upbringing the standard of life of people. With the constant development of new architecture, models, and an increase in the computational capability of the system, the adoption of machine learning and deep learning for activity recognition has shown great improvement with high performance in recent years. My research goal in this thesis is to design and compare machine learning and deep learning models for activity recognition through videos collected from different media in the field of sports. Human activity recognition (HAR) mostly is to recognize the action performed by a human through the data collected from different sources automatically. Based on the literature review, most data collected for analysis is based on time series data collected through different sensors and video-based data collected through the camera. So firstly, our research analyzes and compare different machine learning and deep learning architecture with sensor-based data collected from an accelerometer of a smartphone place at different position of the human body. Without any hand-crafted feature extraction methods, we found that deep learning architecture outperforms most of the machine learning architecture and the use of multiple sensors has higher accuracy than a dataset collected from a single sensor. Secondly, as collecting data from sensors in real-time is not feasible in all the fields such as sports, we study the activity recognition by using the video dataset. For this, we used two state-of-the-art deep learning architectures previously trained on the big, annotated dataset using transfer learning methods for activity recognition in three different sports-related publicly available datasets. Extending the study to the different activities performed on a single sport, and to avoid the current trend of using special cameras and expensive set up around the court for data collection, we developed our video dataset using sports coverage of basketball games broadcasted through broadcasting media. The detailed analysis and experiments based on different criteria such as range of shots taken, scoring activities is presented for 8 different activities using state-of-art deep learning architecture for video classification

Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms

Article originally published International Journal of Machine Learning and ComputingSmartphones 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

Predicting Countermovement Jump Heights By Time Domain, Frequency Domain, and Machine Learning Algorithms

© 2017 IEEE. In this paper, we introduce an experiment evaluating performance of football players in countermovement jumps (CMJs). Three methods including time domain, frequency domain, and machine learning algorithms are proposed for performance evaluation. Correlation coefficients and p-values are given for time domain and frequency domain methods, and prediction errors are given for different machine learning algorithms

Comparative study of machine learning and deep learning architecture for human activity recognition using accelerometer data

© 2018, International Association of Computer Science and Information Technology. Human activity recognition (HAR) has been a popular fields of research in recent times. Many approaches have been implemented in literature with the aim of recognizing and analyzing human activity. Classical machine learning approaches use hand-crafted feature extraction and are based on classification technique, however of late, deep learning approaches have shown greater success in recognition accuracy with increased performance. With the current, wide popularity of mobile phones and various sensors such as accelerometers, gyroscopes, and cameras that are already installed on mobile phones, the activity recognition using the accumulating data from mobile phones has been a significant area of research in HAR. In this paper, we investigate the HAR based on the data collected through the accelerometer sensor of mobile devices. We employ different machine learning (ML) classifiers, algorithms, and deep learning (DL) models across different benchmark datasets. The experimental results from this study provide a comparative performance analysis based on accuracy, performance, and the costs of different ML algorithms and DL algorithms, based on recurrent neural network (RNN) and convolutional neural network (CNN) models for activity recognition

Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms

Smartphones are widely used today, and it becomes possible to detect the user\u27s 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