Sensors for Human Activity Recognition II
Human activity recognition (HAR) has been playing an increasingly important role in the digital age. High-quality sensory observations applicable to recognizing users' activities, whether through external or internal (wearable) sensing technology, are inseparable from sensors' sophisticated design a...
সংরক্ষণ করুন:
| বিন্যাস: | Online |
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| ভাষা: | ইংরেজি |
| প্রকাশিত: |
MDPI - Multidisciplinary Digital Publishing Institute
2025
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | ONIX_20250220_9783725828036_440 |
| ট্যাগগুলো: |
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| _version_ | 1869530648481366016 |
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| collection | Directory of Open Access Books |
| description | Human activity recognition (HAR) has been playing an increasingly important role in the digital age. High-quality sensory observations applicable to recognizing users' activities, whether through external or internal (wearable) sensing technology, are inseparable from sensors' sophisticated design and appropriate application. Having been studied and verified adequately, traditional sensors suitable for human activity recognition—such as external sensors for smart homes, optical sensors like cameras for capturing video signals, and bioelectrical and biomechanical sensors for wearable applications—continue to be researched in depth for more effective and efficient usage. Here, the number of specific areas of life facilitated by sensor-based HAR has been continuously increasing. Meanwhile, innovative sensor research for HAR is also very active in the academic community, including brand new types of sensors appropriate for HAR, new designs and applications of the abovementioned traditional sensors, and the introduction of non-traditional HAR-related sensor types into HAR tasks, among other research avenues. The Special Issue Sensors for Human Activity Recognition has received a total of 30 submissions so far, and from these, this new edition will publish 10 academic articles. From hardware to software, from pipelines to applications, from handcrafted features to domain generalization, and from shallow learning to deep and even large language models, the collected literature will provide readers in related fields with state-of-the-art approaches to many challenges in HAR. |
| format | Online |
| id | doab-20.500.12854ir-153076 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1530762025-02-20T13:31:21Z Sensors for Human Activity Recognition II Liu, Hui Gamboa, Hugo Schultz, Tanja internet of things prototyping process energy-saving interactive design user behaviors ambient agents artificial intelligence deep learning counting weakly labeled data variable length size non-uniform shape data acrophobia virtual reality body movement machine learning sensor network Human Activity Recognition Domain Generalization regularization accelerometer mental effort multimodal physiological signals sensor fusion neuroergonomics human–machine interaction wearable sensors particulate matter activity recognition low-cost sensors participatory research automatic facial expression recognition naturalistic context multimodal large language model device-free behavioral sensing orthogonal signal interference user identification graph neural network scene understanding activities of daily living analysis segmentation feature fusion multi-layer perceptron Yeo–Johnson n/a thema EDItEUR::M Medicine and Nursing::MJ Clinical and internal medicine::MJC Diseases and disorders::MJCL Oncology thema EDItEUR::P Mathematics and Science Human activity recognition (HAR) has been playing an increasingly important role in the digital age. High-quality sensory observations applicable to recognizing users' activities, whether through external or internal (wearable) sensing technology, are inseparable from sensors' sophisticated design and appropriate application. Having been studied and verified adequately, traditional sensors suitable for human activity recognition—such as external sensors for smart homes, optical sensors like cameras for capturing video signals, and bioelectrical and biomechanical sensors for wearable applications—continue to be researched in depth for more effective and efficient usage. Here, the number of specific areas of life facilitated by sensor-based HAR has been continuously increasing. Meanwhile, innovative sensor research for HAR is also very active in the academic community, including brand new types of sensors appropriate for HAR, new designs and applications of the abovementioned traditional sensors, and the introduction of non-traditional HAR-related sensor types into HAR tasks, among other research avenues. The Special Issue Sensors for Human Activity Recognition has received a total of 30 submissions so far, and from these, this new edition will publish 10 academic articles. From hardware to software, from pipelines to applications, from handcrafted features to domain generalization, and from shallow learning to deep and even large language models, the collected literature will provide readers in related fields with state-of-the-art approaches to many challenges in HAR. 2025-02-20T13:31:19Z 2025-02-20T13:31:19Z 2025 book ONIX_20250220_9783725828036_440 9783725828036 9783725828043 https://directory.doabooks.org/handle/20.500.12854/153076 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/10390 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-2804-3 10.3390/books978-3-7258-2804-3 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725828036 9783725828043 208 Basel open access |
| spellingShingle | internet of things prototyping process energy-saving interactive design user behaviors ambient agents artificial intelligence deep learning counting weakly labeled data variable length size non-uniform shape data acrophobia virtual reality body movement machine learning sensor network Human Activity Recognition Domain Generalization regularization accelerometer mental effort multimodal physiological signals sensor fusion neuroergonomics human–machine interaction wearable sensors particulate matter activity recognition low-cost sensors participatory research automatic facial expression recognition naturalistic context multimodal large language model device-free behavioral sensing orthogonal signal interference user identification graph neural network scene understanding activities of daily living analysis segmentation feature fusion multi-layer perceptron Yeo–Johnson n/a thema EDItEUR::M Medicine and Nursing::MJ Clinical and internal medicine::MJC Diseases and disorders::MJCL Oncology thema EDItEUR::P Mathematics and Science Sensors for Human Activity Recognition II |
| title | Sensors for Human Activity Recognition II |
| title_full | Sensors for Human Activity Recognition II |
| title_fullStr | Sensors for Human Activity Recognition II |
| title_full_unstemmed | Sensors for Human Activity Recognition II |
| title_short | Sensors for Human Activity Recognition II |
| title_sort | sensors for human activity recognition ii |
| topic | internet of things prototyping process energy-saving interactive design user behaviors ambient agents artificial intelligence deep learning counting weakly labeled data variable length size non-uniform shape data acrophobia virtual reality body movement machine learning sensor network Human Activity Recognition Domain Generalization regularization accelerometer mental effort multimodal physiological signals sensor fusion neuroergonomics human–machine interaction wearable sensors particulate matter activity recognition low-cost sensors participatory research automatic facial expression recognition naturalistic context multimodal large language model device-free behavioral sensing orthogonal signal interference user identification graph neural network scene understanding activities of daily living analysis segmentation feature fusion multi-layer perceptron Yeo–Johnson n/a thema EDItEUR::M Medicine and Nursing::MJ Clinical and internal medicine::MJC Diseases and disorders::MJCL Oncology thema EDItEUR::P Mathematics and Science |
| topic_facet | internet of things prototyping process energy-saving interactive design user behaviors ambient agents artificial intelligence deep learning counting weakly labeled data variable length size non-uniform shape data acrophobia virtual reality body movement machine learning sensor network Human Activity Recognition Domain Generalization regularization accelerometer mental effort multimodal physiological signals sensor fusion neuroergonomics human–machine interaction wearable sensors particulate matter activity recognition low-cost sensors participatory research automatic facial expression recognition naturalistic context multimodal large language model device-free behavioral sensing orthogonal signal interference user identification graph neural network scene understanding activities of daily living analysis segmentation feature fusion multi-layer perceptron Yeo–Johnson n/a thema EDItEUR::M Medicine and Nursing::MJ Clinical and internal medicine::MJC Diseases and disorders::MJCL Oncology thema EDItEUR::P Mathematics and Science |
| url | ONIX_20250220_9783725828036_440 |