Non-invasive physiological monitoring of exercise and fitness

Burkow-Heikkinen, Lori

doi:10.1179/1743132810y.0000000014

Cited by 17 publications

(9 citation statements)

References 53 publications

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“…For instance, 39.4% of adolescents and 55.9% of adults regularly took part in light-to-moderate PA, an activity that has many health benefits in any age group [7]. In addition, only 7.2% of the youth population and 17.2% of the adult population routinely checked their pulse when exercising, a method that provides an effective monitoring of health-promoting physical activity [37]. Comparable to intervention’s effects on dietary habits, the follow-up results suggest that a single education session may induce changes in health-enhancing PA behaviors.…”

Section: Discussionmentioning

confidence: 99%

Guidelines-Driven Educational Intervention Promotes Healthy Lifestyle Among Adolescents and Adults: A Serbian National Longitudinal Study

Đorđić

Bozic²,

Milanović

et al. 2019

Medicina

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Background and objectives: The effectiveness of short-term focused educational programs to change health behaviors across large populations seems to be poorly described so far. The main aim of the present study was to evaluate an age-specific 45-min educational program, designed in accordance with the current U.S. Department of Health and Human Services and U.S. Department of Agriculture dietary guidelines and physical activity (PA) guidelines, among adolescents and adults. Materials and Methods: We evaluated the health-promoting lifestyle habits by the Health-Promoting Lifestyle Profile (HPLP-II) at baseline and following 6–8 weeks post-education in a nationally representative sample of Serbian adolescents and adults (n = 3822). Results: The percentage of adolescents eating 3–5 servings of vegetables per day increased at follow-up (20.1% versus 23.1%, p = 0.001), with significantly more adolescents regularly reading food labels (from 12.2% at baseline to 14.2% at follow-up; p = 0.02). Taken together, mean HPLP-II scores in adolescents significantly improved for both diet (0.05 points; p < 0.0001) and PA (0.09 points; p < 0.0001), and for PA in adults (0.08 points; p < 0.0001). Hierarchical multiple regression analysis revealed that our model as a whole (including time of testing as a predictor variable, and age and gender as control variables) explained 3.0% of the variance in mean HPLP-II scores for diet (p = 0.942) and 3.0% for PA (p = 0.285) in adolescents, and 1.1% of the variance in HPLP-II scores for diet (p = 0.781) and 1.9% for PA (p = 0.075) in adults, respectively. Conclusions: It appears that a brief focused education can positively tackle unhealthy lifestyles in promoting good health in general population. Different modes of interactive communication used here appeared to strengthen participants’ capacities for lifestyle changes.

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Section: Discussionmentioning

confidence: 99%

Guidelines-Driven Educational Intervention Promotes Healthy Lifestyle Among Adolescents and Adults: A Serbian National Longitudinal Study

Đorđić

Bozic²,

Milanović

et al. 2019

Medicina

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“…Wireless HR monitors are widely available and provide users with real-time feedback of their HR at rest, during and following physical and/or mental exertion [9, 10]. However, wireless HR monitors often require wearing a telemetric strap around the thoracic region or arm, maintaining vigilance, and making intermittent adjustments of the device to ensure continued proper placement.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Smartphone Heart Rate Acquisition Application for Health Promotion and Wellness Telehealth Applications

Gregoski

Mueller

Vertegel

et al. 2012

International Journal of Telemedicine and Applications

148

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Objective. Current generation smartphones' video camera technologies enable photoplethysmographic (PPG) acquisition and heart rate (HR) measurement. The study objective was to develop an Android application and compare HRs derived from a Motorola Droid to electrocardiograph (ECG) and Nonin 9560BT pulse oximeter readings during various movement-free tasks. Materials and Methods. HRs were collected simultaneously from 14 subjects, ages 20 to 58, healthy or with clinical conditions, using the 3 devices during 5-minute periods while at rest, reading aloud under observation, and playing a video game. Correlation between the 3 devices was determined, and Bland-Altman plots for all possible pairs of devices across all conditions assessed agreement. Results. Across conditions, all device pairs showed high correlations. Bland-Altman plots further revealed the Droid as a valid measure for HR acquisition. Across all conditions, the Droid compared to ECG, 95% of the data points (differences between devices) fell within the limits of agreement. Conclusion. The Android application provides valid HRs at varying levels of movement free mental/perceptual motor exertion. Lack of electrode patches or wireless sensor telemetric straps make it advantageous for use in mobile-cell-phone-delivered health promotion and wellness programs. Further validation is needed to determine its applicability while engaging in physical movement-related activities.

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“…sEMG sensors can collect data during daily human activities such as sitting, standing, walking and climbing, which can be used to detect anomalies, actions recognition, etc [12]. sEMG signals are being employed in different applications such as automatic control of lower and upper limb prosthesis or exoskeletons [13], diagnosis of neuromuscular disorders [14] and exercise, fitness monitoring [15]. Khimraj et al classified six movements of lower limb and compared the results of the computational classifiers [16].…”

Section: X-ray Technique Is Primary Used For Gathering and Evaluationmentioning

confidence: 99%

Human knee abnormality detection from imbalanced sEMG data

Vijayvargiya

Prakash

Kumar

et al. 2021

Biomedical Signal Processing and Control

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The classification of imbalanced datasets, especially in medicine, is a major problem in data mining. Such a problem is evident in analyzing normal and abnormal subjects about knee from data collected during walking. In this work, surface electromyography (sEMG) data were collected during walking from the lower limb of 22 individuals (11 with and 11 without knee abnormality). Subjects with a knee abnormality take longer to complete the walking task than healthy subjects. Therefore, the SEMG signal length of unhealthy subjects is longer than that of healthy subjects, resulting in a problem of imbalance in the collected sEMG signal data. Thus, the development of a classification model for such datasets is challenging due to the bias towards the majority class in the data. The collected sEMG signals are challenging due to the contribution of multiple motor units at a time and their dependency on neuromuscular activity, physiological and anatomical properties of the involved muscles. Hence, automated analysis of such sEMG signals is an arduous task. A multi-step classification scheme is proposed in this research to overcome this limitation. The Wavelet Denoising (WD) scheme is used to denoise the collected sEMG signals, followed by the extraction of eleven time-domain features. The oversampling techniques are then used to balance the data under analysis

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Non-invasive physiological monitoring of exercise and fitness

Cited by 17 publications

References 53 publications

Guidelines-Driven Educational Intervention Promotes Healthy Lifestyle Among Adolescents and Adults: A Serbian National Longitudinal Study

Guidelines-Driven Educational Intervention Promotes Healthy Lifestyle Among Adolescents and Adults: A Serbian National Longitudinal Study

Development and Validation of a Smartphone Heart Rate Acquisition Application for Health Promotion and Wellness Telehealth Applications

Human knee abnormality detection from imbalanced sEMG data

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