L773:1566 2535 OR L773:1872 6305
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Revolutionizing healthcare : IoMT-enabled digital enhancement via multimodal ADL data fusion
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- Ghayvat, Hemant (author)
- Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM),Linnaeus University, Växjo, Sweden
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- Awais, Muhammad (author)
- Univ Texas MD Anderson Canc Ctr, USA,The University Of Texas Md Anderson Cancer Center, Houston, United States
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- Geddam, Rebakah (author)
- Nirma Univ, India,Institute Of Technology, Nirma University, Ahmedabad, India
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- Amsterdam : Elsevier, 2024
- 2024
- English.
- In: Information Fusion. - Amsterdam : Elsevier. - 1566-2535 .- 1872-6305. ; 111
- Journal article (peer-reviewed)
Abstract
Subject headings
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- The present research develops a framework to refine the classification of an individual's activities and recognize wellness associated with their routine. The framework improves the accuracy of the classification of routine activities of a person, the activation time data of sensors fixed on objects linked with the routine activities of the person, and the aptness of an incessant activity pattern with the routine activities. The existing techniques need continuous monitoring and are non-adaptive to a person's persistent habitual variations or individualities. The research involves applying Internet of Medical Things (IoMT)-based sensor information fusion to the novel multimodel data analytics to develop Activities of Daily Living (ADL) pattern, behavioral pattern generation and anomaly recognition. The novel multimodel data analytics approach is named AiCareLiving. AicareLiving is an IoMT and artificial intelligence (AI) enabled approach. The research work describes activity data using an individual's activities within a specified area before evaluating the activity data to detect the existence of an anomaly by identifying the deviation of the activity data from the activity profile, which indicates the anticipated behavior and activity of the person. This wellness information would be shared to the caregivers, related healthcare professionals, care providers and municipalities through the secured healthcare information exchange protocol and IoMT. AiCareLiving framework aims to least false positive in terms of anomaly detection and forecasting; the high precision is close to the confidence level of 95%.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Multi-Sensor Modalities data
- Sensor information fusion
- IoMT
- AIoMT
- Behavioral pattern generation
- Ambient assisted living
- Digitally enhanced
- ADL
- Information fusion
- Computer Science
- Datavetenskap
Publication and Content Type
- ref (subject category)
- art (subject category)
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