International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022
p-ISSN: 2395-0072
www.irjet.net
Human Activity Recognition Using Neural Network Swapnaja Jadhav1, Tejas Dalal2, Karan Pawar3, Atul Kulkarni4, Arif Manyar5 Department of Computer Engineering, DYPIT Pimpri, Pune
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Abstract - Human activity recognition can be found in a
hand-crafted neural network model for recognizing human activities is presented in this study. Selecting meaningful features from the provided time and frequency domain characteristics is made easier with the help of an algorithm developedusing neighborhood component analysis. Afterward, a four-layer deep neural network is utilized to classify the input data into several groups. The fact that we were able to outperform most previous models despite utilizing fewer features shows just how important feature selection is. When compared to existing state-of-the-art methods, our proposed model outperformed the majority of other methods while using less features, demonstrating the critical nature of feature selection. The model was evaluated using a publicly available dataset of six daily activities from the UCI Health Risk Assessment (HAR).
variety of study domains, including medical organizations, survey systems, security monitoring, and human computer interface. This paper provides a viable technique to identifying six common human- centered actions (walking, sitting, standing, squat, punch and moving head) using Logistic Regression, Logistic Regression CV, and the CNN algorithm. A precise and pleasurable computer application that sense human body movements to acquire context information. As a repository, an activity recognition database is regarded publicly available in this case. Key Words: CNN Algorithm
1. INTRODUCTION
Asmita Nandy, Jayita Saha, Chandreyee Chowdhury, Kundan P.D. Singh” Detailed Human Activity Recognition using Wearable Sensor and Smartphones”[2] Human activity detection is increasingly being employed in smart homes, eldercare, and remote health monitoring and surveillance. To better serve these goals, actions suchas sitting in a chair or on the floor, taking a slow or brisk stroll, jogging with a weight, and so on must berecognized comprehensively. Few studies haveattempted to differentiate between hard activities(such as walking while carrying a heavy burden) and their inverse (walking), which is crucial for effective health monitoring of the elderly and patientsrecovering from surgery. The usage of wearable and smartphone-embedded sensors has been presented asa solution for this goal in this work. As a result, the contribution of this work is to create an ensemble of classifiers to provide a framework for precise identification of static and dynamic activities, as well as their intensive equivalents. The ensemble is configured so that test instances are classified using weighted majority voting. The basis classifiers' outputperformance for the training dataset is sent into a neural network to determine their weights. We determined that our work has a recognition accuracy of greater than 94%.
With the increasing rise in the need for security and surveillance, particularly in crowded areas like airports, shopping malls and social gatherings, the problem of human detection and activity recognition has attained importance in the vision community. Human activity recognition is an important area of computer vision research and applications. The goalof the activity recognition is an automated analysis or interpretation of ongoing events and their context from video data. Its applications include surveillance systems, patient monitoring systems, and a variety of systems that involve interactions between persons and electronic devices such as human-computer interfaces. Most of these applications require recognition of high level activities, often composed ofmultiple simple actions of person’s lifestyle
2. MOTIVATION The aims of Human-centered computing are to appreciate individual activities with their social perspective. Importantly the classification performance of the learned model using new data set as compared to the previous one, with reduced set of features and improved results
Mohanad Babiker, Othman O. khalifa, Kyaw Kyaw Htike , Aisha Hassan, Muhamed Zaharadeen,” Automated Daily Human Activity Recognition for Video Surveillance Using Neural Network”[3] Due to consumer needs for security, surveillance video systems are garnering growing attention in the field ofcomputer vision. Observing human movement and predicting such senses of movement is promising. The need arises to design a surveillance system capable of overcoming the limitation of relying on human resources to continuously watch, observe, and record normal and suspicious events
3. LITURETURE SURVEY Syed K. Bashar, Md Abdullah Al Fahim and Ki H. Chon” Smartphone Based Human Activity Recognition with Feature Selection and Dense Neural Network”[1]Human activity recognition (HAR) has grown in prominence in recent years due to the embedded sensors in smartphones, with applications in healthcare, surveillance, human-device interactions, and pattern identification. An activity- driven
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