Smartphone PPG: signal processing, quality assessment, and impact on HRV parameters

Tyapochkin, Konstantin; Smorodnikova, Evgeniya; Pravdin, Pavel

doi:10.1109/embc.2019.8856540

Cited by 14 publications

(11 citation statements)

References 15 publications

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“…Data acquisition RR-interval sequences from 1,471 subjects were obtained through the Welltory app, which subjects used to take heart rate variability measurements with their smartphone cameras or Apple Watch while in a resting state, after completing a stress self-assessment questionnaire (Perceived Stress Questionnaire (PSQ)) on the same day. We excluded measurements that showed possible arrhythmias in research subjects [23] , as well as low-quality measurements [24] , because these factors can significantly distort HRV metrics. The RR-interval sequences were used to calculate AMo, pNN50, and MedSD values.…”

Section: Methodsmentioning

confidence: 99%

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Smartphone App Stress Assessments: Heart Rate Variability vs Perceived Stress in a Large Group of Adults

Tyapochkin¹,

Kovaleva²,

Smorodnikova³

et al. 2020

Preprint

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BackgroundMultiple studies have shown that the state of stress has a negative impact on decision-making, the cardiovascular system, and the autonomic nervous system [1]. In light of this, we have developed a mobile application in order to assess user stress levels based on the state of their physiological systems. This assessment is based on heart rate variability [2], [3], [4], [5], which many wearable devices such as Apple Watch have learned to measure in the background. We developed a proprietary algorithm that assesses stress levels based on heart rate variability analysis, and this research paper shows that assessments positively correlate with subjective feelings of stress experienced by users.ObjectiveThe objective of this paper is to study the relationship between HRV-based physiological stress responses and Perceived Stress Questionnaire self-assessments in order to validate Welltory measurements as a tool that can be used for daily stress measurements.SettingWe analyzed data from Welltory app users, which is publicly available and free of charge. The app allows users to complete the Perceived Stress Questionnaire and take heart rate variability measurements, either with Apple Watch or with their smartphone cameras.SubjectsTo conduct our study, we collected all questionnaire results from users between the ages of 25 and 60 who also took a heart rate variability measurement on the same day, after filling out the Questionnaire. In total, this research paper includes results from 1,471 participants (602 men and 869 women).MeasurementsWe quantitatively measured physiological stress based on AMo, pNN50, and MedSD values, which were calculated based on sequences of RR-intervals recorded with the Welltory app. We assessed psychological stress levels based on the Perceived Stress Questionnaire (PSQ) [6], [7].ResultsPhysiological stress reliably correlates with self-assessed psychological stress levels - low for subjects with low psychological stress levels, medium for subjects with medium psychological stress levels, and high for subjects with high psychological stress levels. On a scale of 0-100%, median physiological stress is 48.7 (95% CI of 45.2-50.7%), 56.4 (95% CI of 54.3-58.9), and 62.5 (95% CI of 59.7-66.3) for these groups, respectively.ConclusionsPhysiological stress response, which is calculated based on heart rate variability analysis, on average increases as psychological stress increases. Our results show that HRV measurements significantly correlate with perceived psychological stress, and can therefore be used as a stress assessment tool.

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Section: Methodsmentioning

confidence: 99%

“…4. The quality of the heart rate variability measurements taken by participants was high enough (measurement quality is calculated based on data obtained from the measurement device [24] ).…”

Section: Methodsmentioning

confidence: 99%

Smartphone App Stress Assessments: Heart Rate Variability vs Perceived Stress in a Large Group of Adults

Tyapochkin¹,

Kovaleva²,

Smorodnikova³

et al. 2020

Preprint

Self Cite

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“…Using the phones cameras as a PPG sensors has also been thoroughly investigated in the literature. A popular approach leverages the PPG technique to monitor heart activity when a user places the finger on the smartphone camera [6], [7], [47]- [53], similar to the principle of oximeters. However, such approaches require active user participation and their accuracy depends on multiple factors, including precise finger placement with respect to the camera [7].…”

Section: Related Workmentioning

confidence: 99%

“…However, such models are accessible only to a limited number of users. To relax the requirement for custom hardware or high-end phones with special sensors, [6], [7] introduced solutions that use a standard smartphone camera to measure heart rate using the PPG technique. However, users are required to place the finger on the camera, with the accuracy tightly coupled to the precise placement [7].…”

Section: Introductionmentioning

confidence: 99%

“…To relax the requirement for custom hardware or high-end phones with special sensors, [6], [7] introduced solutions that use a standard smartphone camera to measure heart rate using the PPG technique. However, users are required to place the finger on the camera, with the accuracy tightly coupled to the precise placement [7]. To remove the requirement for physical contact, [8] introduced a solution for computing heart rate variability using a video of the user's face captured by the smartphone front camera.…”

Section: Introductionmentioning

confidence: 99%

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HandRate: Heart Rate Monitoring While Simply Holding a Smartphone

Jiokeng

Jakllari

Beylot

2021

2021 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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We present HandRate, the first smartphone-based system using a standard sensor (accelerometer) for opportunistically computing heart rate while a user holds their phone. Fundamentally, HandRate revisits ballistocardiography (BCG), a century-old technique for monitoring heart activity by measuring the body movement caused by the cardiac cycle. Traditionally performed using custom hardware, attached to a subject's body, revisiting BCG for the smartphone, held in hand, faces several challenges. The hand is an external organ furthest from the aorta and subject to motion artifacts, leading to a weak and noisy signal, while the position the phone is held in can impact which accelerometer axis best captures BCG. HandRate addresses these challenges by introducing a design involving two modules operating in tandem: the first aimed at transforming the accelerometer readings into a single-dimensional signal oblivious to how the phone is held, while the second module making heartbeat predictions based on this signal. Results from testing HandRate using data collected from 18 subjects show that it can estimate heart rate with accuracy similar to or better than systems requiring special sensors and/active user participation.

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Outsourcing multiauthority access control revocation and computations over medical data to mobile cloud

Koe

Chen

Tang

et al. 2022

Int J of Intelligent Sys

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With recent advances in cloud computing, mobile devices are increasingly being used to record patient physiological parameters, and transfer them to a cloudbased hospital information system, for access control mediation over a variety of stakeholders. In such a cloud-based architecture, the patient must specify an access policy for a group of authorized parties towards its outsourced data. Multiauthority ciphertext-policy attribute-based encryption (CP-ABE) was provided as an innovative cloud-based access control cryptographic primitive to tackle the key escrow issue in a centralized architecture, and boost flexibility through cross-domain attributes management. Existing works, however, still have glaring drawbacks. First, they still rely on a trusted authority to generate and distribute user secret keys. Second, they do not simultaneously provide encryption, decryption, or revocation outsourcing, resulting in high processing and communication cost for both the data sender and the data receiver. Third, they do not support both user and attribute revocation, and the integrity of ciphertext downloaded from the cloud is not always verified at the user end. As a result, this paper exploits the dummy attribute technique and introduces a novel, efficient, and secure multiauthority

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Smartphone PPG: signal processing, quality assessment, and impact on HRV parameters

Cited by 14 publications

References 15 publications

Smartphone App Stress Assessments: Heart Rate Variability vs Perceived Stress in a Large Group of Adults

Smartphone App Stress Assessments: Heart Rate Variability vs Perceived Stress in a Large Group of Adults

HandRate: Heart Rate Monitoring While Simply Holding a Smartphone

Outsourcing multiauthority access control revocation and computations over medical data to mobile cloud

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