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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.02869 |
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| _version_ | 1866913799985954816 |
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| author | Sonawane, Mayur Dhayalkar, Sahil Rajesh Waje, Siddesh Markhelkar, Soyal Wattamwar, Akshay Shrawne, Seema C. |
| author_facet | Sonawane, Mayur Dhayalkar, Sahil Rajesh Waje, Siddesh Markhelkar, Soyal Wattamwar, Akshay Shrawne, Seema C. |
| contents | Human Activity Recognition is a subject of great research today and has its applications in remote healthcare, activity tracking of the elderly or the disables, calories burnt tracking etc. In our project, we have created an Android application that recognizes the daily human activities and calculate the calories burnt in real time. We first captured labeled triaxial acceleration readings for different daily human activities from the smartphone's embedded accelerometer. These readings were preprocessed using a median filter. 42 features were extracted using various methods. We then tested various machine learning algorithms along with dimensionality reduction. Finally, in our Android application, we used the machine learning algorithm and a subset of features that provided maximum accuracy and minimum model building time. This is used for real-time activity recognition and calculation of calories burnt using a formula based on Metabolic Equivalent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_02869 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Human Activity Recognition using Smartphones Sonawane, Mayur Dhayalkar, Sahil Rajesh Waje, Siddesh Markhelkar, Soyal Wattamwar, Akshay Shrawne, Seema C. Machine Learning Artificial Intelligence Human Activity Recognition is a subject of great research today and has its applications in remote healthcare, activity tracking of the elderly or the disables, calories burnt tracking etc. In our project, we have created an Android application that recognizes the daily human activities and calculate the calories burnt in real time. We first captured labeled triaxial acceleration readings for different daily human activities from the smartphone's embedded accelerometer. These readings were preprocessed using a median filter. 42 features were extracted using various methods. We then tested various machine learning algorithms along with dimensionality reduction. Finally, in our Android application, we used the machine learning algorithm and a subset of features that provided maximum accuracy and minimum model building time. This is used for real-time activity recognition and calculation of calories burnt using a formula based on Metabolic Equivalent. |
| title | Human Activity Recognition using Smartphones |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2404.02869 |