Pre-clinical physiological data acquisition and testing of the IMAGE sensing device for exercise guidance and real-time monitoring of cardiovascular disease patients

Astaras, A.; Kokonozi, Athina; Michail, Emmanouil; Filos, Dimitrios; Chouvarda, Ioanna; Grossenbacher, Olivier; Koller, J. M.; Leopoldo, R.; Porchet, J-A; Correvon, Marc; Luprano, Jean; Sipila, Auli; Maglaveras, Nicos

doi:10.1007/978-3-642-13039-7_60

Cited by 5 publications

(3 citation statements)

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“…Compared with currently available wearable-devicebased exercise guidance systems, the proposed smart-clothing-based intelligent exercise guidance system has some novel features [11][12][13][14][15][16][17][18][19]. The proposed intelligent exercise guidance system incorporated music beats, which no current exercise guidance systems have done [11][12][13][14][15][16][17][18][19]. In addition, the proposed intelligent exercise guidance system used smart clothing for ECG signal collection and HR monitoring, which current exercise guidance systems also have not done [11][12][13][14][15][16][17][18][19].…”

Section: Discussionmentioning

confidence: 99%

“…The proposed intelligent exercise guidance system incorporated music beats, which no current exercise guidance systems have done [11][12][13][14][15][16][17][18][19]. In addition, the proposed intelligent exercise guidance system used smart clothing for ECG signal collection and HR monitoring, which current exercise guidance systems also have not done [11][12][13][14][15][16][17][18][19]. The authors believe that the proposed smartclothing-based intelligent exercise guidance system may provide more effective and personalized guidance for exercises.…”

Section: Discussionmentioning

confidence: 99%

“…Wearable-device-based exercise guidance systems have thus been proposed in recent years to resolve this problem [11][12][13][14][15][16][17][18][19]. Astaras et al [11] and Kokonozi et al [12] presented prototypes of a wearable dry electrode device designed for exercise guidance and real-time monitoring of patients with CVD. Balsalobre-Fernandez et al [13] introduced a wearable band for measuring movement velocity during a back squat exercise.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Exercise Guidance System Based on Smart Clothing

et al. 2018

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In this study, we designed and developed an intelligent exercise guidance system based on smart clothing. The system comprised smart clothing for electrocardiogram (ECG) signal acquisition and heart rate (HR) monitoring, an exercise control application program, and a cloud server. Music beats were used to guide the exercise routine. The use of an empirical mode decomposition (EMD)-based ECG signal denoising algorithm and a quadratic polynomial regression model (QPRM) of HR and running cadence (running steps per minute guided by music beats) were proposed for the system. Five types of experiments were conducted: Experiments I and II, R-peak detection; Experiment III, preset QPRMs; Experiment IV, degree of completion of exercises; and Experiment V, comparison of preset and trained QPRMs. The average accuracy and sensitivity of the EMD-based R-peak detection method were respectively 99.8% and 94.87% for ECG data from the MIT-BIH Arrhythmia Database and 96.46% and 98.75% for ECG data collected from university students during the walking exercise. The coefficient of determination and the mean absolute percentage error (MAPE) of the QPRMs were respectively 97.21% and 3.12% for increasing HR and 98.09% and 2.06% for decreasing HR. The average degrees of completion for warmup, training, and cooldown exercise stages were 97.05%, 91.91%, and 98.32%, respectively. The MAPEs of the preset and trained QPRMs were respectively 6.37% and 3.84% for increasing HR and 5.25% and 3.57% for decreasing HR. The experimental results demonstrated the effectiveness of the proposed system in exercise guidance.

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