An Investigation of Heartrate Sensing Accuracy by Wrist-Worn Fitness Tracking Devices

Turki, Ahmad; Jani, Mahrshi B.; Ding, Kan; Zhang, Rong; Behbehani, Khosrow

doi:10.1109/embc.2019.8856375

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Furthermore, [13] highlights the absence of a standard test protocol for the validation process. As [14] and [15] report, compared to an ambulatory electrocardiogram (ECG), two commercially available models shown to have high accuracy, giving heartbeat accuracy of less than ±10% for the 24-hour period and across all activities. Additionally, both devices were less accurate following erratic movements and increased heart rate.…”

Section: Related Researchmentioning

confidence: 99%

Semantic Constraints Specification and Schematron-Based Validation for Internet of Medical Things’ Data

2022

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Fitness and activity trackers are hugely popular wearable devices that monitor various health-related metrics, such as step count, heartbeat rate, or even oxygen saturation. Utilizing personal health information obtained by users' personal trackers provides promising results in the fields of telemedicine and personal well-being. However, we face challenges such as data quality, privacy and compliance with standards and regulations. This paper addresses such challenges, with the focus on the last one. Semantic constraints for healthcare datatypes are defined to ensure compliance with standards, making the information medically valid and relevant. A process of semantic verification and Schematronbased validation is proposed. The validation process suggested in this paper will enable the data to be transferred and incorporated into a formal Electronic Health Record. The process is then verified using datasets containing various healthrelated data types. The aim is to integrate personal health data into Electronic Health Record, which forms a part of Central Health Information System. This would provide personalized medical services to patients and help physicians to make more informed decisions.

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Section: Related Researchmentioning

confidence: 99%

Semantic Constraints Specification and Schematron-Based Validation for Internet of Medical Things’ Data

2022

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“…It is, however, not suitable for long-term measurement because it causes stress. Several capacitive coupling methods have been reported for non-contact measurement of heart-rate, but they suffer from weak detection signals [ 7 , 8 , 9 ]. Another popular method is to use microwave or millimeter wave Doppler radar as a non-contact heart-rate measurement system [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Non-Contact Heart-Rate Measurement Method Using Both Transmitted Wave Extraction and Wavelet Transform

Yang

Mitsui

Wang

et al. 2021

Sensors

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Continuous monitoring of heart-rate is expected to lead to early detection of physical discomfort. In this study, we propose a non-contact heart-rate measurement method which can be used in an environment such as driver heart-rate monitoring with body movement. The method is based on the electric field strength transmitted through the human body that changes with the diastole and systole of the heart. Unlike conventional displacement detection of the skin surface, we attempted to capture changes in the internal structure of the human body by irradiating the human body with microwaves and acquiring microwaves that pass through the heart. We first estimated the electric field strength transmitted through the heart using three receiving sensors to reduce the body movement effect. Then we decomposed the estimated transmitted electric field using stationary wavelet transform to eliminate significant distortion due to body movement. As a result, we achieved an estimation accuracy of heart-rate as high as 98% in a verification experiment with normal body movement.

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Determination of Phobic Disorders Using Virtual Reality Technologies

Obukhov

Dedov

Chebotov

et al. 2022

Software Engineering Perspectives in Systems

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An Investigation of Heartrate Sensing Accuracy by Wrist-Worn Fitness Tracking Devices

Cited by 6 publications

References 10 publications

Semantic Constraints Specification and Schematron-Based Validation for Internet of Medical Things’ Data

Semantic Constraints Specification and Schematron-Based Validation for Internet of Medical Things’ Data

Non-Contact Heart-Rate Measurement Method Using Both Transmitted Wave Extraction and Wavelet Transform

Determination of Phobic Disorders Using Virtual Reality Technologies

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