Wearable, Cuff-less PPG-Based Blood Pressure Monitor with Novel Height Sensor

Shaltis,; Reisner,; Asada,

doi:10.1109/iembs.2006.4397550

Cited by 15 publications

(15 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Blood glucose levels can be read by a device such as IBGStar [39] which is plugged directly into the mobile phone and can digitize blood glucose test strip results. Wearable ECG and blood pressure [40], [41] sensors can be used to track user health. The number of possibilities in this space is growing rapidly and wearable devices are expected to be at least a 6 B$ market by 2016 [42].…”

Section: A System Capabilities 1) Acquisition Of Additional Contextumentioning

confidence: 99%

FEEL: A System for Frequent Event and Electrodermal Activity Labeling

Ayzenberg

Picard

2014

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

Abstract-The wide availability of low-cost wearable biophysiological sensors enables us to measure how the environment and our experiences impact our physiology. This creates a challenge: in order to interpret the longitudinal data, we require the matching contextual information as well. Collecting continuous biophysiological data makes it unfeasible to rely solely on our memory for contextual information. In this paper, we first present an architecture and implementation of a system for the acquisition, processing, and visualization of biophysiological signals and contextual information. Next, we present the results of a user study: users wore electrodermal activity wrist sensors that measured their autonomic arousal. These users uploaded the sensor data at the end of each day. At first, they annotated their events at the end of each day; then, after a two-day break, they annotated the data from two days earlier. One group of users had access to both the signal and the contextual information collected by the mobile phone and the other group could only access the biophysiological signal. At the end of the study, the users filled in a system usability scale and user experience surveys. Our results show that the system enables the users to annotate biophysiological signals at a greater effectiveness than the current state of the art while also providing very good usability.

show abstract

Section: A System Capabilities 1) Acquisition Of Additional Contextumentioning

confidence: 99%

FEEL: A System for Frequent Event and Electrodermal Activity Labeling

Ayzenberg

Picard

2014

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

show abstract

“…Recent developments focus on measurements that cause the least amount of discomfort to patients, as improved convenience leads to higher acceptance rate [2]. An ideal sensor is thus non-invasive, light-weight, unobtrusive, and trivial to place, which point towards cuffless, optical methods [3].…”

Section: Introductionmentioning

confidence: 99%

Data-driven estimation of blood pressure using photoplethysmographic signals

Gao

Wittek

Zhao

et al. 2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

View full text Add to dashboard Cite

Abstract-Noninvasive measurement of blood pressure by optical methods receives considerable interest, but the complexity of the measurement and the difficulty of adjusting parameters restrict applications. We develop a method for estimating the systolic and diastolic blood pressure using a single-point optical recording of a photoplethysmographic (PPG) signal. The estimation is data-driven, we use automated machine learning algorithms instead of mathematical models. Combining supervised learning with a discrete wavelet transform, the method is insensitive to minor irregularities in the PPG waveform, hence both pulse oximeters and smartphone cameras can record the signal. We evaluate the accuracy of the estimation on 78 samples from 65 subjects (40 male, 25 female, age 29±7) with no history of cardiovascular disease. The estimate for systolic blood pressure has a mean error 4.9±4.9 mm Hg, and 4.3±3.7 mm Hg for diastolic blood pressure when using the oximeter-obtained PPG. The same values are 5.1±4.3 mm Hg and 4.6±4.3 mm Hg when using the phone-obtained PPG, comparing with A&D UA-767PBT result as gold standard. The simplicity of the method encourages ambulatory measurement, and given the ease of sharing the measured data, we expect a shift to data-oriented approaches deriving insight from ubiquitous mobile devices that will yield more accurate machine learning models in monitoring blood pressure.

show abstract

“…[7][8][9][10] However, there are often several unique challenges to designing and implementing a system capable of monitoring physical activity without the use of large equipment. 10 As Corbishley and Rodriguez-Villegas 11 acknowledge, an ideal system must use small sensors, such that they do not interfere with the individual's movement pattern. The size of the device has important implications for its function, including how big the battery must be, the weight of the sensors selected, and the system's processing power.…”

Section: Introductionmentioning

confidence: 99%

Accuracy and Precision of an Accelerometer-Based Smartphone App Designed to Monitor and Record Angular Movement over Time

Bittel

Elazzazi

Bittel

2016

Telemedicine and e-Health

View full text Add to dashboard Cite

"Accuracy and precision of an accelerometer-based smartphone app designed to monitor and record angular movement over time. " Telemedicine and e-Health.22,4. 302-309. (2016 AbstractBackground: Therapeutic exercise is a central component in the management of many common conditions. It is imperative, therefore, that clinicians monitor and correct patient performance to facilitate the use of proper form both in the clinic and during home exercise programs. Although clinicians are trained to prescribe exercise and analyze form, there are many subtleties that may be missed by relying on visual assessment. This study investigated the accuracy and precision of a novel, exercise-training smartphone application (app), running on an iPhone Ò (Apple, Cupertino, CA) 4

show abstract

Wearable, Cuff-less PPG-Based Blood Pressure Monitor with Novel Height Sensor

Cited by 15 publications

References 0 publications

FEEL: A System for Frequent Event and Electrodermal Activity Labeling

FEEL: A System for Frequent Event and Electrodermal Activity Labeling

Data-driven estimation of blood pressure using photoplethysmographic signals

Accuracy and Precision of an Accelerometer-Based Smartphone App Designed to Monitor and Record Angular Movement over Time

Contact Info

Product

Resources

About