A review of wearable and unobtrusive sensing technologies for chronic disease management

Guo, Yao; Liu, Xiangyu; Peng, Sijia; Jiang, Xinyu; Xu, Ke; Chen, Chen; Wang, Zeyu; Dai, Chenyun; Chen, Wei

doi:10.1016/j.compbiomed.2020.104163

Cited by 68 publications

(35 citation statements)

References 114 publications

(148 reference statements)

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“…A solution could be combinations of more accurate pedometers for activity recognition, with smartwatches. With this combination, higher accuracy can be obtained, however, it would be more impractical as the use of these wearables should be as unobtrusive and discrete as possible [52] - [53].…”

Section: Methodsmentioning

confidence: 99%

COVID-19 Diagnosis at Early Stage Based on Smartwatches and Machine Learning Techniques

et al. 2021

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Early detection of COVID-19 positive people are now extremely needed and considered to be one of the most effective ways how to limit spreading the infection. Commonly used screening methods are reverse transcription polymerase chain reaction (RT-PCR) or antigen tests, which need to be periodically repeated. This paper proposes a methodology for detecting the disease in non-invasive way using wearable devices and for the analysis of bio-markers using artificial intelligence. This paper have reused a publicly available dataset containing COVID-19, influenza, and Healthy control data. In total 27 COVID-19 positive and 27 healthy control were pre-selected for the experiment, and several feature extraction methods were applied to the data. This paper have experimented with several machine learning methods, such as XGBoost, knearest neighbour k-NN, support vector machine, logistic regression, decision tree, and random forest, and statistically evaluated their perfomance using various metrics, including accuracy, sensitivity and specificity. The proposed experiment reached 78 % accuracy using the k-NN algorithm which is significantly higher than reported for state-of-the-art methods. For the cohort containing influenza, the accuracy was 73 % for k-NN. Additionally, we identified the most relevant features that could indicate the changes between the healthy and infected state. The proposed methodology can complement the existing RT-PCR or antigen screening tests, and it can help to limit the spreading of the viral diseases, not only COVID-19, in the non-invasive way.

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Section: Methodsmentioning

confidence: 99%

COVID-19 Diagnosis at Early Stage Based on Smartwatches and Machine Learning Techniques

et al. 2021

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“…The standard technique to read HR consists of analyzing the time interval between two consecutive R wave peaks detected (QRS complex). Other data reading techniques that also consider the use of HR as a metric are ballistocardiography (BCG), phonocardiography (PCG), and impedance cardiography (ICG) [ 15 ].…”

Section: Physiological Variables Of Prevalent Diseases In Older Adultsmentioning

confidence: 99%

“…(3) By the FDA-approval level achieved, if any, in the works presented by Wang et al [ 7 ], Leirós-Rodríguez et al [ 8 ], Rucco et al [ 9 ], Stavropoulos et al [ 10 ], and Tun et al [ 11 ], no type of FDA approval was proposed. Likewise, others works have identified the monitoring of patients with chronic degenerative diseases [ 12 , 13 , 14 , 15 , 16 ], wearables for promoting physical activities (the deficiency of physical activity has been determined as a crucial influence in developing chronic diseases) [ 17 , 18 , 19 ], movement disorders (e.g., Parkinson’s disease, freezing of gait) [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ], development of sensors and wearable technologies [ 32 , 33 , 34 , 35 , 36 ], wearable device use evaluations [ 8 , 10 , 37 , 38 , 39 , 40 , 41 , 42 ], measurement of biomedical variables and parameters [ 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ], and other works have reviewed reviews related to wearables appli...…”

Section: Introductionmentioning

confidence: 99%

Remote Healthcare for Elderly People Using Wearables: A Review

et al. 2022

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The growth of health care spending on older adults with chronic diseases faces major concerns that require effective measures to be adopted worldwide. Among the main concerns is whether recent technological advances now offer the possibility of providing remote health care for the aging population. The benefits of suitable prevention and adequate monitoring of chronic diseases by using emerging technological paradigms such as wearable devices and the Internet of Things (IoT) can increase the detection rates of health risks to raise the quality of life for the elderly. Specifically, on the subject of remote health monitoring in older adults, a first approach is required to review devices, sensors, and wearables that serve as tools for obtaining and measuring physiological parameters in order to identify progress, limitations, and areas of opportunity in the development of health monitoring schemes. For these reasons, a review of articles on wearable devices was presented in the first instance to identify whether the selected articles addressed the needs of aged adults. Subsequently, the direct review of commercial and prototype wearable devices with the capability to read physiological parameters was presented to identify whether they are optimal or usable for health monitoring in older adults.

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“…Wearable devices, video devices, and other human internet-of-things devices add end intelligence to effectively improve the proactivity, timeliness, and stability of the collection of data. For example, they provide local intelligence services for endpoint devices using field programmable gate array capsulation [4,5,12] . With the rapid development of 5G and fog-end computing nodes, they are used in the vicinity of patients, reducing the loss in data transmission.…”

Section: Medical Ai Based On Wearable Devicesmentioning

confidence: 99%

Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare

Xie

Gao

et al. 2021

CURR MED SCI

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Chronic diseases are a growing concern worldwide, with nearly 25% of adults suffering from one or more chronic health conditions, thus placing a heavy burden on individuals, families, and healthcare systems. With the advent of the “Smart Healthcare” era, a series of cutting-edge technologies has brought new experiences to the management of chronic diseases. Among them, smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state. However, how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management, in terms of quality of life, patient outcomes, and privacy protection, is an urgent issue that needs to be addressed. Artificial intelligence (AI) can provide intelligent suggestions by analyzing a patient’s physiological data from wearable devices for the diagnosis and treatment of diseases. In addition, blockchain can improve healthcare services by authorizing decentralized data sharing, protecting the privacy of users, providing data empowerment, and ensuring the reliability of data management. Integrating AI, blockchain, and wearable technology could optimize the existing chronic disease management models, with a shift from a hospital-centered model to a patient-centered one. In this paper, we conceptually demonstrate a patient-centric technical framework based on AI, blockchain, and wearable technology and further explore the application of these integrated technologies in chronic disease management. Finally, the shortcomings of this new paradigm and future research directions are also discussed.

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A review of wearable and unobtrusive sensing technologies for chronic disease management

Cited by 68 publications

References 114 publications

COVID-19 Diagnosis at Early Stage Based on Smartwatches and Machine Learning Techniques

COVID-19 Diagnosis at Early Stage Based on Smartwatches and Machine Learning Techniques

Remote Healthcare for Elderly People Using Wearables: A Review

Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare

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