Logical reasoning for human activity recognition based on multisource data from wearable device
Abstract Smart wearable devices detection and recording of people’s everyday activities is critical for health monitoring, helping persons with disabilities, and providing care for the elderly. Most of the research that is being conducted uses a machine learning-based methodology; however, these app...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84532-8 |
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| author | Mahmood Alsaadi Ismail Keshta Janjhyam Venkata Naga Ramesh Divya Nimma Mohammad Shabaz Nirupma pathak Pavitar Parkash Singh Sherzod Kiyosov Mukesh Soni |
| author_facet | Mahmood Alsaadi Ismail Keshta Janjhyam Venkata Naga Ramesh Divya Nimma Mohammad Shabaz Nirupma pathak Pavitar Parkash Singh Sherzod Kiyosov Mukesh Soni |
| author_sort | Mahmood Alsaadi |
| collection | DOAJ |
| description | Abstract Smart wearable devices detection and recording of people’s everyday activities is critical for health monitoring, helping persons with disabilities, and providing care for the elderly. Most of the research that is being conducted uses a machine learning-based methodology; however, these approaches frequently have issues with high computing resource consumption, burdensome training data gathering, and restricted scalability across many contexts. This research suggests a behaviour detection technology based on multi-source sensing and logical reasoning to address these problems. In order to realize the natural fusion of signal processing and logical reasoning in behavior recognition research, this work designs a lightweight behavior recognition solution using the pertinent theories of ontology reasoning in classical artificial intelligence. Machine learning technology is also employed for behavior recognition using the same data set. Once the best model has been chosen, the cross-person recognition results after testing and modification of parameters are 90.8% and 92.1%, respectively. This technology was used to create a behaviour recognition system, and several tests were run to assess how well it worked. The findings demonstrate that the suggested strategy achieves over 90% recognition accuracy for 11 different daily activities, including jogging, walking, and stair climbing. Additionally, the suggested strategy dramatically minimises the quantity of user-provided training data needed in comparison to machine learning-based behaviour identification techniques. |
| format | Article |
| id | doaj-art-0a37d3fd6a6f486e857a594a04748f09 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0a37d3fd6a6f486e857a594a04748f092025-01-05T12:13:52ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-024-84532-8Logical reasoning for human activity recognition based on multisource data from wearable deviceMahmood Alsaadi0Ismail Keshta1Janjhyam Venkata Naga Ramesh2Divya Nimma3Mohammad Shabaz4Nirupma pathak5Pavitar Parkash Singh6Sherzod Kiyosov7Mukesh Soni8Department of Computer Sciences, College of Sciences, University of Al MaarifComputer Science and Information Systems Department, College of Applied Sciences, AlMaarefa UniversityDepartment of CSE, Graphic Era Hill UniversityData Analyst in UMMC, University of Southern MississippiModel Institute of Engineering and TechnologyCSE-R Department, KL University Andhra PradeshDepartment of Management, Lovely Professional UniversityThe Department of Tax and Taxation, Tashkent State University of EconomicsDr. D. Y. Patil Vidyapeeth, Pune, Dr. D. Y. Patil School of Science & TechnologyAbstract Smart wearable devices detection and recording of people’s everyday activities is critical for health monitoring, helping persons with disabilities, and providing care for the elderly. Most of the research that is being conducted uses a machine learning-based methodology; however, these approaches frequently have issues with high computing resource consumption, burdensome training data gathering, and restricted scalability across many contexts. This research suggests a behaviour detection technology based on multi-source sensing and logical reasoning to address these problems. In order to realize the natural fusion of signal processing and logical reasoning in behavior recognition research, this work designs a lightweight behavior recognition solution using the pertinent theories of ontology reasoning in classical artificial intelligence. Machine learning technology is also employed for behavior recognition using the same data set. Once the best model has been chosen, the cross-person recognition results after testing and modification of parameters are 90.8% and 92.1%, respectively. This technology was used to create a behaviour recognition system, and several tests were run to assess how well it worked. The findings demonstrate that the suggested strategy achieves over 90% recognition accuracy for 11 different daily activities, including jogging, walking, and stair climbing. Additionally, the suggested strategy dramatically minimises the quantity of user-provided training data needed in comparison to machine learning-based behaviour identification techniques.https://doi.org/10.1038/s41598-024-84532-8Wearable devicesLogical reasoningHuman activity recognitionMultisource dataIMUData signal |
| spellingShingle | Mahmood Alsaadi Ismail Keshta Janjhyam Venkata Naga Ramesh Divya Nimma Mohammad Shabaz Nirupma pathak Pavitar Parkash Singh Sherzod Kiyosov Mukesh Soni Logical reasoning for human activity recognition based on multisource data from wearable device Scientific Reports Wearable devices Logical reasoning Human activity recognition Multisource data IMU Data signal |
| title | Logical reasoning for human activity recognition based on multisource data from wearable device |
| title_full | Logical reasoning for human activity recognition based on multisource data from wearable device |
| title_fullStr | Logical reasoning for human activity recognition based on multisource data from wearable device |
| title_full_unstemmed | Logical reasoning for human activity recognition based on multisource data from wearable device |
| title_short | Logical reasoning for human activity recognition based on multisource data from wearable device |
| title_sort | logical reasoning for human activity recognition based on multisource data from wearable device |
| topic | Wearable devices Logical reasoning Human activity recognition Multisource data IMU Data signal |
| url | https://doi.org/10.1038/s41598-024-84532-8 |
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