International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 08|Aug 2023
p-ISSN: 2395-0072
www.irjet.net
A Novel Approach for Machine Learning-Based Identification of Human Activities Asst.Prof. Shruthi1, Sujata2 1
Asst. Professor, Dept. of Computer Science and Engineering, Sharnbasva University, Kalaburagi ,Karnataka, India 2 Student, Dept. of Artificial Intelligence and Data science, Sharnbasva University, Kalaburagi ,Karnataka, India -----------------------------------------------------------------------***--------------------------------------------------------------------------
Abstract
Key words: Human activity recognition, machine learning, datasets, feature extraction, model training, healthcare, sports, security.
to entertainment, the ability to automatically identify and classify human activities is reshaping how we interact with our environment. The nucleus of this pioneering endeavor lies in the synthesis of machine learning and human activity datasets. These datasets, meticulously annotated with activity labels, serve as the building blocks upon which the machinery of recognition is constructed. By training machine learning models on these datasets, a transformative process unfolds. These models become adept at deciphering the subtle patterns, temporal relationships, and spatial cues that define human actions. The canvas of this project is woven with intricate threads, each representing a distinct facet of its essence. The initial phase involves the strategic curation of datasets, each containing a panorama of human activities captured and labeled with meticulous care. These datasets, often drawn from real-world scenarios, harbor the invaluable knowledge needed to train models that can mirror humanlike recognition capabilities. Intriguingly, the scope of this project's implications is vast and far-reaching. Healthcare stands to benefit from automated patient monitoring, identifying anomalies in daily routines. In the realm of sports analysis, the models provide coaches with insights into athletes' performance nuances. Security and surveillance embrace a new paradigm, where machineaugmented vigilance ensures safety with unmatched efficiency. As we embark on this journey into the realm of human activity recognition through machine learning, we set forth to unravel the intricate tapestry of human behavior. Our aim is to harness the synergy of technology and cognition, creating a bridge between the two that resonates across various applications. This project encapsulates the essence of innovation, where the fusion of data, algorithms, and human-centric understanding creates a paradigm shift that redefines how we perceive and interact with the world around us.
1. INTRODUCTION
2. Related Works
In an era defined by the convergence of technology and human activities, the realm of human activity recognition stands as a beacon of innovation. This groundbreaking field amalgamates the power of machine learning algorithms with the intricacies of human actions, creating a dynamic interplay that holds immense potential across diverse domains. From healthcare to sports, and security
Article[1] "Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey" by Mohamed Abdelwahab, Abdelkader Hamouda, and Abdelkrim Ait Mohamed in 2020, offers a thorough exploration of human activity recognition (HAR) through inertial, physiological, and environmental sensors. The study delves into various
Human activity recognition (HAR) is a rapidly growing field of research that uses machine learning to automatically identify and classify human activities from sensor data. This data can be collected from a variety of sources, such as wearable sensors, smartphones, and video cameras. HAR has a wide range of potential applications, including healthcare, sports, and security. In this paper, we present a comprehensive overview of the state-of-the-art in HAR using machine learning based on datasets. We discuss the various feature extraction techniques that can be applied, and the different machine learning algorithms that can be used for model training. We also present a survey of the recent literature on HAR using machine learning, and we discuss the challenges and opportunities that lie ahead in this field. Our findings suggest that HAR using machine learning based on datasets is a promising approach for a variety of applications. However, there are still a number of challenges that need to be addressed in order to improve the accuracy and robustness of HAR systems. These challenges include the need for more accurate and efficient feature extraction techniques, the development of more powerful machine learning algorithms, and the creation of larger and more diverse datasets. We believe that this paper provides a valuable contribution to the field of HAR using machine learning. It provides a comprehensive overview of the stateof-the-art, and it identifies the challenges and opportunities that lie ahead. We hope that this paper will help to accelerate the development of more accurate and reliable HAR systems that can be used to improve the lives of people in a variety of ways.
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