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Leveraging Machine ...
Leveraging Machine Learning for Disease Diagnoses based on Wearable Devices : A Survey
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- Jiang, Zhihan (författare)
- IoT Lab, Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, Hong Kong, (CHN)
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- van Zoest, Vera, 1992- (författare)
- Uppsala universitet,Försvarshögskolan,Avdelningen för försvarssystem,Reglerteknik,Datorteknik,Avdelningen för systemteknik
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- Deng, Weipeng (författare)
- IoT Lab, Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, Hong Kong, (CHN)
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- Ngai, Edith. C. H. (författare)
- IoT Lab, Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, Hong Kong, (CHN)
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- Liu, Jiangchuan (författare)
- Department of Computing, Simon Fraser University, Burnaby, Canada, (CAN)
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2023
- 2023
- Engelska.
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 10:24, s. 21959-21981
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Many countries around the world are facing a shortage of healthcare resources, especially during the post-epidemic era, leading to a dramatic increase in the need for self-detection and self-management of diseases. The popularity of smart wearable devices, such as smartwatches, and the development of machine learning bring new opportunities for the early detection and management of various prevalent diseases, such as cardiovascular diseases, Parkinson’s disease, and diabetes. In this survey, we comprehensively review the articles related to specific diseases or health issues based on small wearable devices and machine learning. More specifically, we first present an overview of the articles selected and classify them according to their targeted diseases. Then, we summarize their objectives, wearable device and sensor data, machine learning techniques, and wearing locations. Based on the literature review, we discuss the challenges and propose future directions from the perspectives of privacy concerns, security concerns, transmission latency and reliability, energy consumption, multi-modality, multi-sensor, multi-devices, evaluation metrics, explainability, generalization and personalization, social influence, and human factors, aiming to inspire researchers in this field.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Information Systems (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Annan medicin och hälsovetenskap -- Övrig annan medicin och hälsovetenskap (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Other Medical and Health Sciences -- Other Medical and Health Sciences not elsewhere specified (hsv//eng)
Nyckelord
- Försvarssystem
- Systems science for defence and security
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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