Smartwatch: Performance evaluation for long-term heart rate monitoring

Phan, Dung H.; Siong, Lee Yee; Pathirana, Pubudu N.; Seneviratne, Aruna

doi:10.1109/isbb.2015.7344944

Cited by 85 publications

(57 citation statements)

References 20 publications

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“…This approach is, however, not very robust to the environment [96]. Some vendors apply so-called photoplethysmography (PPG) to determine whether a wearable device (e.g., a smartwatch) is currently on its user's wrist or not [97,98]. The process is similar to the one followed when measuring heart rate [99].…”

Section: Hand Geometrymentioning

confidence: 99%

Multi-Factor Authentication: A Survey

et al. 2018

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Today, digitalization decisively penetrates all the sides of the modern society. One of the key enablers to maintain this process secure is authentication. It covers many different areas of a hyper-connected world, including online payments, communications, access right management, etc. This work sheds light on the evolution of authentication systems towards Multi-Factor Authentication (MFA) starting from Single-Factor Authentication (SFA) and through Two-Factor Authentication (2FA). Particularly, MFA is expected to be utilized for human-to-everything interactions by enabling fast, user-friendly, and reliable authentication when accessing a service. This paper surveys the already available and emerging sensors (factor providers) that allow for authenticating a user with the system directly or by involving the cloud. The corresponding challenges from the user as well as the service provider perspective are also reviewed. The MFA system based on reversed Lagrange polynomial within Shamir's Secret Sharing (SSS) scheme is further proposed to enable more flexible authentication. This solution covers the cases of authenticating the user even if some of the factors are mismatched or absent. Our framework allows for qualifying the missing factors by authenticating the user without disclosing sensitive biometric data to the verification entity. Finally, a vision of the future trends in MFA is discussed.

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Section: Hand Geometrymentioning

confidence: 99%

Multi-Factor Authentication: A Survey

et al. 2018

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“…Wearable devices allow non-invasive monitoring of physiological activity. In particular, smartwatches can be applied to different contexts, such as stress [4], sleep quality [19] or physical fitness [5] to measure well-being through heart rate. Moreover, these devices are often equipped with a range of additional sensors, allowing for more holistic measurements.…”

Section: Consumer Wearable Devicesmentioning

confidence: 99%

“…Smartwatches thus collect a magnitude of data, most of which is sufficient for giving their user an overview of their daily activity. However, when being applied to more serious medical and healthcare use cases, the current measurement accuracy of wearable devices remains rather unclear [5,6,9,10,20].…”

Section: Consumer Wearable Devicesmentioning

confidence: 99%

Improving heart rate variability measurements from consumer smartwatches with machine learning

Maritsch

Bérubé

Kraus

et al. 2019

Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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The reactions of the human body to physical exercise, psychophysiological stress and heart diseases are reflected in heart rate variability (HRV). Thus, continuous monitoring of HRV can contribute to determining and predicting issues in well-being and mental health. HRV can be measured in everyday life by consumer wearable devices such as smartwatches which are easily accessible and affordable. However, they are arguably accurate due to the stability of the sensor. We hypothesize a systematic error which is related to the wearer movement. Our evidence builds upon explanatory and predictive modeling: we find a statistically significant correlation between error in HRV measurements and the wearer movement. We show that this error can be minimized by bringing into context additional available sensor information, such as accelerometer data. This work demonstrates our research-in-progress on how neural learning can minimize the error of such smartwatch HRV measurements.

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“…Because the measurement of HR in unit of beats using ECG and the exact representation of heart activity in waveforms are possible as shown in Figure 8, accurate monitoring of HR variability is possible [36,37]. ECG plays a very important role in the diagnosis of cardiac diseases from myocardial ischemia and infarction to syncope and palpitations.…”

Section: Biological Datamentioning

confidence: 99%

Biological-Signal-Based User-Interface System for Virtual-Reality Applications for Healthcare

2018

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Biosignal interfaces provide important data that reveal the physical status of a user, and they are used in the medical field for patient health status monitoring, medical automation, or rehabilitation services. Biosignals can be used in developing new contents, in conjunction with virtual reality, and are important factors for extracting user emotion or measuring user experience. A biological-signal-based user-interface system composed of sensor devices, a user-interface system, and an application that can extract biological-signal data from multiple biological-signal devices and be used by content developers was designed. A network-based protocol was used for unconstrained use of the device so that the biological signals can be freely received via USB, Bluetooth, WiFi, and an internal system module. A system that can extract biological-signal data from multiple biological-signal data and simultaneously extract and analyze the data from a virtual-reality-specific eye-tracking device was developed so that users who develop healthcare contents based on virtual-reality technology can easily use the biological signals.

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Smartwatch: Performance evaluation for long-term heart rate monitoring

Cited by 85 publications

References 20 publications

Multi-Factor Authentication: A Survey

Multi-Factor Authentication: A Survey

Improving heart rate variability measurements from consumer smartwatches with machine learning

Biological-Signal-Based User-Interface System for Virtual-Reality Applications for Healthcare

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