A study on measuring heart- and respiration-rate via wrist-worn accelerometer-based seismocardiography (SCG) in comparison to commonly applied technologies

Haescher, Marian; Matthies, Denys J. C.; Trimpop, John; Urban, Bodo

doi:10.1145/2790044.2790054

Cited by 42 publications

(24 citation statements)

References 12 publications

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“…As it can be seen in Fig. 2, the size of the sensor is 16.5 × 29.1 × 8.5 mm 3 that in comparison with other works in [10,14,15], the developed sensor benefits less size. Also, low-weighting (20 g) allows users to carry it easily, being portable.…”

Section: Healthy Measurementioning

confidence: 84%

“…In this Letter, a small wireless sensor for heartbeat and respiration rate monitoring based on chest movements (or SCG) is developed. The component of the developed sensor consists of a microcontroller, an accelerometer as well as a transceiver that all of them are attached to a printed circuit board (PCB) benefitting from low-profile and low-weight features compared with other methods in [10,14,15]. In comparison to another SCG system in [10,14,15], the proposed system can accurately estimate heartbeat and respiration rate.…”

mentioning

confidence: 99%

“…Therefore, the rootmean-square error (RMSE) of the proposed system are obtained as 2.27 for heartbeat and 0.93 for respiration rate. Moreover, the system is able to estimate the initial problems of the cardiorespiratory system, while the other works in [10,14,15] do not have this advantage.…”

mentioning

confidence: 99%

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Developed wearable miniature sensor to diagnose initial perturbations of cardiorespiratory system

Abbasi-Kesbi

Nikfarjam

Hezaveh

2018

Healthcare Technology Letters

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The progress of microelectromechanical systems tends to fabricate miniature motion sensors that can be used for various purposes of biomedical systems, particularly on-body applications. A miniature wireless sensor is developed that not only monitors heartbeat and respiration rate based on chest movements but also identifies initial problems in the cardiorespiratory system, presenting a healthy measure defined based on height and length of the normal distribution of respiration rate and heartbeat. The obtained results of various tests are compared with two commercial sensors consisting of electrocardiogram sensor as well as belt sensor of respiration rate as a reference (gold standard), showing that the root-mean-square errors obtain <2.27 beats/min for a heartbeat and 0.93 breaths/min for respiration rate. In addition, the standard deviation of the errors reaches <1.26 and 0.63 for heartbeat and respiration rates, separately. According to the outcome results, the sensor can be considered an appropriate candidate for in-home health monitoring, particularly early detection of cardiovascular system problems.

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Section: Healthy Measurementioning

confidence: 84%

mentioning

confidence: 99%

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Developed wearable miniature sensor to diagnose initial perturbations of cardiorespiratory system

Abbasi-Kesbi

Nikfarjam

Hezaveh

2018

Healthcare Technology Letters

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“…The developed system provided rapid and appropriate directions in emergency situations and alerted the user or caregiver to manage the changes in the patient's health condition. 28,29 In one study, 30 the authors proposed a system on falls prevention, detection, and biofeedback monitoring using ubiquitous devices. The system used infrared sensors or vision cameras to detect falls.…”

Section: Data Analysis Using Smartphone Applicationsmentioning

confidence: 99%

Clinical decision support systems in hospital care using ubiquitous devices: Current issues and challenges

Baig

GholamHosseini

Moqeem

et al. 2017

Health Informatics J

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Supporting clinicians in decision making using advanced technologies has been an active research area in biomedical engineering during the past years. Among a wide range of ubiquitous systems, smartphone applications have been increasingly developed in healthcare settings to help clinicians as well as patients. Today, many smartphone applications, from basic data analysis to advanced patient monitoring, are available to clinicians and patients. Such applications are now increasingly integrating into healthcare for clinical decision support, and therefore, concerns around accuracy, stability, and dependency of these applications are rising. In addition, lack of attention to the clinicians' acceptability, as well as the low impact on the medical professionals' decision making, are posing more serious issues on the acceptability of smartphone applications. This article reviews smartphone-based decision support applications, focusing on hospital care settings and their overall impact of these applications on the wider clinical workflow. Additionally, key challenges and barriers of the current ubiquitous device-based healthcare applications are identified. Finally, this article addresses current challenges, future directions, and the adoption of mobile healthcare applications.

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“…Many sensors have been proposed and implemented to detect a user’s respiratory rate, including respiration belts worn around the chest, mask-like spirometers, skin-based photoplethysmography, or body-worn inertial sensors, such as in [ 6 ]. Most methods require physical contact to the user’s body, which tends to become burdensome, uncomfortable, or restraining for the person to wear such sensors over longer time periods.…”

Section: Introductionmentioning

confidence: 99%

Towards Breathing as a Sensing Modality in Depth-Based Activity Recognition

Kempfle

Laerhoven

2020

Sensors

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Depth imaging has, through recent technological advances, become ubiquitous as products become smaller, more affordable, and more precise. Depth cameras have also emerged as a promising modality for activity recognition as they allow detection of users’ body joints and postures. Increased resolutions have now enabled a novel use of depth cameras that facilitate more fine-grained activity descriptors: The remote detection of a person’s breathing by picking up the small distance changes from the user’s chest over time. We propose in this work a novel method to model chest elevation to robustly monitor a user’s respiration, whenever users are sitting or standing, and facing the camera. The method is robust to users occasionally blocking their torso region and is able to provide meaningful breathing features to allow classification in activity recognition tasks. We illustrate that with this method, with specific activities such as paced-breathing meditating, performing breathing exercises, or post-exercise recovery, our model delivers a breathing accuracy that matches that of a commercial respiration chest monitor belt. Results show that the breathing rate can be detected with our method at an accuracy of 92 to 97% from a distance of two metres, outperforming state-of-the-art depth imagining methods especially for non-sedentary persons, and allowing separation of activities in respiration-derived features space.

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A study on measuring heart- and respiration-rate via wrist-worn accelerometer-based seismocardiography (SCG) in comparison to commonly applied technologies

Cited by 42 publications

References 12 publications

Developed wearable miniature sensor to diagnose initial perturbations of cardiorespiratory system

Developed wearable miniature sensor to diagnose initial perturbations of cardiorespiratory system

Clinical decision support systems in hospital care using ubiquitous devices: Current issues and challenges

Towards Breathing as a Sensing Modality in Depth-Based Activity Recognition

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