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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
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Issue Published : 114
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| Paper Title: | Smart-Fit : Recognition and Classification using Machine Learning |
| Authors Name: | Aryaman Rajesh Karmarkar , Anurag Vijay Bobde , Ayushi Liladharrao Kolhe , Abhishek Nachankar , Gaurav Anil Karadbhajne |
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IJRTI_188683
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| Published Paper Id: | IJRTI2312043 |
| Published In: | Volume 8 Issue 12, December-2023 |
| DOI: | |
| Abstract: | Human Activity recognition is most thrilled and adventurous field of research area and scientifically recognized development about the computer vision, image-processing, human – computer interaction and various real-time human activities. It is a prominent area of analyzing and classification of the real time physical activities. Real time activities can be performed in different fields like healthcare, childcare, sports, fitness, security. In the previous scenarios, processing data has been recorded through IOT based sensors or extracted manually from datasets from official websites. Detailed overview of previous years, reference papers on human activity recognition is discussed in this paper. Automatic recognition of the human physical activity has become crucial in inter-personal communication and human behavior analysis. Taking this topic to the next level upgradation in our project, in this paper we are using the smartphone recorded dataset through Google-Fit app. As we know, now-a-days smartphones are playing important role in recognizing activities and have inbuilt fitness sensors like accelerometer, gyroscope, magnetometer and even the gps data. The google-fit data we have extracted have multiple variations and is recorded from the real time scenarios. We applied various machine learning algorithms like logistic regression, random forests, support vector machine, naïve bayes, k-nearest neighbors, gradient boosting methods, neural networks for accuracy prediction and classifying activities. Also, we are using ensemble methods to predict the accuracies of all learning methods on single dataset in a single format. Our experimental results will compare all the techniques and show if we get the best accuracy through the ensemble methods or separately predicting the accuracy by applying each machine learning algorithm independently. We will also calculate the F1 score, precision score and recall score to evaluate the metrics and a Confusion matrix was made for each module. Additionally, we are using action classifier, object recognition and deep learning methods for continuously recording activities and multi-person activity tracking and recognition processes. |
| Keywords: | Google-Fit, Machine Learning, Logistic Regression, Random forests, Support vector machine, Naïve bayes, K-nearest neighbours, Gradient boosting methods, Neural Networks, Ensemble methods. |
| Cite Article: | "Smart-Fit : Recognition and Classification using Machine Learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 12, page no.306 - 314, December-2023, Available :http://www.ijrti.org/papers/IJRTI2312043.pdf |
| Downloads: | 000205201 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 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: IJRTI2312043
Registration ID:188683
Published In: Volume 8 Issue 12, December-2023
DOI (Digital Object Identifier):
Page No: 306 - 314 Country: Nagpur , Maharashtra, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2312043 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2312043 |
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ISSN: 2456-3315
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