2017
DOI: 10.3390/s17122892
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Real-Time Monitoring in Home-Based Cardiac Rehabilitation Using Wrist-Worn Heart Rate Devices

Abstract: Cardiac rehabilitation is a key program which significantly reduces the mortality in at-risk patients with ischemic heart disease; however, there is a lack of accessibility to these programs in health centers. To resolve this issue, home-based programs for cardiac rehabilitation have arisen as a potential solution. In this work, we present an approach based on a new generation of wrist-worn devices which have improved the quality of heart rate sensors and applications. Real-time monitoring of rehabilitation se… Show more

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Cited by 31 publications
(25 citation statements)
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“…For this purpose, we will present data processing of HRS from rehabilitation sessions through three stages. In the first stage, the raw data from heart rate streams is initially preprocessed using a previous approach [22]. In this stage, a fuzzy model is proposed to monitor the heart rate under a linguistic approach in real time by means of three representative terms and their membership functions, low, adequate, and high, as well as short-term fuzzy temporal windows (FTWs).…”
Section: Methodsmentioning
confidence: 99%
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“…For this purpose, we will present data processing of HRS from rehabilitation sessions through three stages. In the first stage, the raw data from heart rate streams is initially preprocessed using a previous approach [22]. In this stage, a fuzzy model is proposed to monitor the heart rate under a linguistic approach in real time by means of three representative terms and their membership functions, low, adequate, and high, as well as short-term fuzzy temporal windows (FTWs).…”
Section: Methodsmentioning
confidence: 99%
“…In work [22], real-time monitoring of heart rate streams was proposed. First, three intuitive terms low, adequate, and high defined with three membership functions by means of fuzzy sets were proposed in order to describe the variable heart rate (HR).…”
Section: Real-time Monitoring Of Heart Rate Streamsmentioning
confidence: 99%
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