A machine learning based sentient multimedia framework to increase safety at work
| العنوان: | A machine learning based sentient multimedia framework to increase safety at work |
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| المؤلفون: | Massimo Callisto De Donato, Gianluca Bonifazi, Domenico Ursino, Emiliano Anceschi, Luca Virgili, Enrico Corradini |
| المصدر: | Multimedia Tools and Applications |
| بيانات النشر: | Springer Science and Business Media LLC, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Internet of things, Industry 4.0, Computer Networks and Communications, Computer science, Decision trees, media_common.quotation_subject, Decision tree, Wearable computer, Context (language use), 1191: Sentient Multimedia Systems, 02 engineering and technology, Machine learning, computer.software_genre, Presentation, Fall detection, 0202 electrical engineering, electronic engineering, information engineering, Media Technology, media_common, business.industry, Safety at work, 020207 software engineering, Hardware and Architecture, Information and Communications Technology, Sentient multimedia systems, Specialization (logic), Multimedia framework, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Software |
| الوصف: | In the last few decades, we have witnessed an increasing focus on safety in the workplace. ICT has always played a leading role in this context. One ICT sector that is increasingly important in ensuring safety at work is the Internet of Things and, in particular, the new architectures referring to it, such as SIoT, MIoT and Sentient Multimedia Systems. All these architectures handle huge amounts of data to extract predictive and prescriptive information. For this purpose, they often make use of Machine Learning. In this paper, we propose a framework that uses both Sentient Multimedia Systems and Machine Learning to support safety in the workplace. After the general presentation of the framework, we describe its specialization to a particular case, i.e., fall detection. As for this application scenario, we describe a Machine Learning based wearable device for fall detection that we designed, built and tested. Moreover, we illustrate a safety coordination platform for monitoring the work environment, activating alarms in case of falls, and sending appropriate advices to help workers involved in falls. |
| تدمد: | 1573-7721 1380-7501 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8615decf52e1299729ef889756324ef5 https://doi.org/10.1007/s11042-021-10984-z |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....8615decf52e1299729ef889756324ef5 |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 15737721 13807501 |
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