摘要(英) |
Heart rate(HR) detection is an important physiological indicator for maintaining health
and improving athletic performance. Wrist-worn optical heart rate devices utilize
photoplethysmography (PPG) technology to measure HR. However, compared to the well
established and stable electrocardiogram (ECG) technology, wrist-worn optical HR detection
using PPG devices has shown inconsistent accuracy across different types of activities,
including rest, exercise of low/medium/high intensity.
The result showed that PPG devices can serve as effective tools for HR monitoring. When
considering the advancements in PPG technology and the convenience of device wearability,
PPG devices are a preferable choice for applications involving daily life, exercise & fitness,
and outdoor & adventures, compared to ECG devices, especially when excluding algorithm
differences among brands.
The exercise test for HR detection involved 32 participants and included various exercise
configurations, such as rest, three intensities of indoor cycling (40/70/100 watts), treadmill
walking (5 km/h)/jogging (7 km/h)/running (9 km/h), and push over. The HR data of the Garmin
Fenix 7s (PPG) wrist-worn device and the HRM-Tri (ECG) chest strap device show a
significant correlation. The overall correlation coefficient, rc is 0.988. When considering
different configurations, the correlation coefficients are as follows: resting configuration rc =
0.915; indoor cycling configuration rc = 0.983; treadmill configuration rc = 0.949; Push over
configuration rc = 0.987. The significance level for all configurations is p < 0.01.
Furthermore, the detailed analysis by combining the Bland-Altman plot with heart rate
zones. Among all configurations, zone 4 exhibits the highest number of outliers. In the resting
configuration, outliers are primarily distributed in zone 1. For the indoor cycling configuration,
zone 4 has the highest number of outliers. Similarly, the treadmill configuration shows the
highest number of outliers in zone 4, while the Push over configuration has the highest number
of outliers in zone 1. |
參考文獻 |
[1] Garmin Taiwan (Director). (2017, November 2). 胸帶式心跳帶與光學心率感測器的差異大解密. https://www.youtube.com/watch?v=NHb7Az8_zvM
[2] Guo Y., Liu X., Peng S., Jiang X., Xu K., Chen C., Wang Z., Dai C., Chen W. (2021).
A review of wearable and unobtrusive sensing technologies for chronic disease management, Computers in Biology and Medicine, 129, 104163.
[3] Lee G. H., Kang H., Chung J. W., Lee Y., Yoo H., Jeong S., Cho H., Kim J. Y., Kang S. G., Jung J. Y., Hahm S. G., Lee J., Jeong I. J., Park M., Park G., Yun I. H., Kim J. Y., Hong Y., Yun Y., Kim S. H., Choi B. K.(2022). Stretchable PPG sensor with light polarization for physical activity–permissible monitoring, Science Advances, 8(15).
[4] Fallow A. B., Tarumi, T. & Tanaka, H. (2013). Influence of skin type and wavelength on light wave reflectance. Journal of Clinical Monitoring and Computing, 27, 313–317.
[5] Merrigan J. J., Stovall J. H., Stone J. D., Stephenson M., Finomore V. S. & Hagen J. A.(2022). Validation of Garmin and Polar Devices for Continuous Heart Rate Monitoring During Common Training Movements in Tactical Populations. Measurement in Physical Education and Exercise Science,1-14.
[6] Lin, L.I.(1989). A Concordance Correlation Coefficient to Evaluate Reproducibility. Biometrics, 45(1), 255-268.
[7] Spierer, D. K., Rossen, Z., Litman, L. L.& Fuji, K. (2015). Validation of photoplethysmography as a method to detect heart rate during rest and exercise. Journal of Medical Engineering & Technology, 39, 264–271.
[8] Alzahrani, A., Hu, S., Azorin-Peris, V., Barrett, L., Esliger, D., Hayes, M.,Kuoch, S. (2015). A multi-channel opto-electronic sensor to accurately monitor heart rate against motion artefact during exercise. Sensors, 15 (10), 25681–25702.
[9] Stahl, S. E., An, H.-S., Dinkel, D. M., Noble, J. M., & Lee, J.-M. (2016). How accurate are the wrist-based heart rate monitors during walking and running activities? Are they accurate enough? BMJ Open Sport & Exercise Medicine, 2(1).
[10] Gillinov, S., Etiwy, M., Wang, R., Blackburn, G., Phelan, D., Gillinov, A. M., HOUGHTALING, P., JAVADIKASGARI, H., Desai, M. Y. (2017). Variable accuracy of wearable heart rate monitors during aerobic exercise. Medicine and Science in Sports and Exercise, 49 (8), 1697–1703.
[11] Støve, M. P., Haucke, E., Nymann, M. L., Sigurdsson T.& Birgit Tine Larsen, B. T. (2019). Accuracy of the wearable activity tracker Garmin Forerunner 235 for the assessment of heart rate during rest and activity. Journal of Sports Sciences, 37(8).
[12] Mukaka, M. M. (2012). A guide to appropriate use of Correlation coefficient in medical research. Malawi Medical Journal, 24(3), 69-71.
[13] Edwards, S. (1993). The heart rate monitor book. Port Washington, NY: Polar CIC.
[14] Carrasco, J. L., Jover L. (2003). Estimating the Generalized Concordance Correlation Coefficient through Variance Components. Biometrics, 59(4), 849-858.
[15] Bland, J.M., Altman, D. G. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, 1(8476):307-10.
[16] 衛生福利部國民健康署. (2016, December 31). https://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=333&pid=882 |