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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Enhancing HAR Model Accuracy through Multimodal Sensor Data Integration |
| Authors Name: | Abhijeet Sonawane , Pratik Chopane , Harshada Shelke , Nikhil Chinchore , Prof. Jaishri Shilpakar |
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IJNRD_197061
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| Published Paper Id: | IJNRD2305699 |
| Published In: | Volume 8 Issue 5, May-2023 |
| DOI: | |
| Abstract: | The rapid development of technology in recent years has led to the production of a large amount of information. This growth in data has led to significant growth in areas such as robotics and the Internet of Things (IoT). This research paper aims to compare and evaluate the use and accuracy of the Human Activity Recognition (HAR) model by focusing on Long Term Memory (LSTM) model. To ensure research reliability and consistency, both models were trained on the same data, including data collected from wearable devices. In addition to reliable research, information is also available from public websites. Accuracy and falsity are measured by comparing samples against a matrix of accuracy and confusion. In addition, the article explores various methods and methods of using sensor data in the general knowledge of people, where these models can be used separately or together. Experimental results show that LSTM models are suitable for different situations and show better convergence than neural networks. In addition to comparative analysis, this article highlights the importance of analyzing human activities and their potential applications in areas such as virtual reality, health, entertainment and security. Use data for human cognitive functions, including monitoring physical activity, monitoring sleep patterns, and analyzing movement for rehabilitation. |
| Keywords: | Human Activity Recognition (HAR), Long-Short Term Memory (LSTM), Wearable sensors, Sensors Data, Accuracy, DOM (Data Object Model) |
| Cite Article: | "Enhancing HAR Model Accuracy through Multimodal Sensor Data Integration", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.g815-g819, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305699.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2305699 Registration ID: 197061 Published In: Volume 8 Issue 5, May-2023 DOI (Digital Object Identifier): Page No: g815-g819 Country: Pune, Maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2305699 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2305699 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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