ReviewJournal of applied physiology (Bethesda, Md. : 1985)2018
Wearable ballistocardiogram and seismocardiogram systems for health and performance.
Review in Journal of applied physiology (Bethesda, Md. : 1985), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed, 74 citations in OpenAlex.
- Reliability of heart rate and respiration rate measurements with a wireless accelerometer in postbariatric recovery.PloS one · 2021Trial
- Clinical Readiness of Hemodynamic Estimation from Cardiac Vibration Signals: An Endpoint-Specific Critical Review.Annals of biomedical engineering · 2026Review
- Article
- Cardiac-vagal rhythm echoes on the heartbeat's mechanosensory imprint in the brain.Communications biology · 2025Article
- A Forcecardiography dataset with simultaneous SCG, Heart Sounds, ECG, and Respiratory signals.Scientific data · 2025Article
- A Flexible PVDF Sensor for Forcecardiography.Sensors (Basel, Switzerland) · 2025Article
- A Deep Convolution Method for Hypertension Detection from Ballistocardiogram Signals with Heat-Map-Guided Data Augmentation.Bioengineering (Basel, Switzerland) · 2025Article
- Prediction of ECG signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervals.Scientific reports · 2025Article
- Wearable Devices Based on Bioimpedance Test in Heart-Failure: Design Issues.Reviews in cardiovascular medicine · 2024Review
- All-in-One, Wireless, Multi-Sensor Integrated Athlete Health Monitor for Real-Time Continuous Detection of Dehydration and Physiological Stress.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Multichannel ballistocardiography: A comparative analysis of heartbeat detection across different body locations.PloS one · 2024Article
- Approaches of wearable and implantable biosensor towards of developing in precision medicine.Frontiers in medicine · 2024Review
- Wearables in Cardiovascular Disease.Journal of cardiovascular translational research · 2023Review
- Linear and Non-Linear Heart Rate Variability Indexes from Heart-Induced Mechanical Signals Recorded with a Skin-Interfaced IMU.Sensors (Basel, Switzerland) · 2023Article
- Non-invasive monitoring of cardiac function through Ballistocardiogram: an algorithm integrating short-time Fourier transform and ensemble empirical mode decomposition.Frontiers in physiology · 2023Article
- Quantitative Analysis Using Consecutive Time Window for Unobtrusive Atrial Fibrillation Detection Based on Ballistocardiogram Signal.Sensors (Basel, Switzerland) · 2022Article
- Enabling Wearable Pulse Transit Time-Based Blood Pressure Estimation for Medically Underserved Areas and Health Equity: Comprehensive Evaluation Study.JMIR mHealth and uHealth · 2021Article
- Non-Invasive Wearable Patch Utilizing Seismocardiography for Peri-Operative Use in Surgical Patients.IEEE journal of biomedical and health informatics · 2021Article
- Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management.Nature reviews. Cardiology · 2021Review
- Novel Digital Technologies for Blood Pressure Monitoring and Hypertension Management.Current cardiovascular risk reports · 2021Review
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Cardiovascular diseases (CVDs) are prevalent in the US, and many forms of CVD primarily affect the mechanical aspects of heart function. Wearable technologies for monitoring the mechanical health of the heart and vasculature could enable proactive management of CVDs through titration of care based on physiological status as well as preventative wellness monitoring to help promote lifestyle choices that reduce the overall risk of developing CVDs. Additionally, such wearable technologies could be used to optimize human performance in austere environments. This review describes our progress in developing wearable ballistocardiogram (BCG)- and seismocardiogram-based systems for monitoring relative changes in cardiac output, contractility, and blood pressure. Our systems use miniature, low-noise accelerometers to measure the movements of the body in response to the heartbeat and novel machine learning algorithms to provide robustness against motion artifacts and sensor misplacement. Moreover, we have mathematically related wearable BCG signals-representing local, cardiogenic movements of a point on the body-to better understood whole body BCG signals, and thereby improved estimation of key health parameters. We validated these systems with experiments in healthy subjects, studies in patients with heart failure, and measurements in austere environments such as water immersion. The systems can be used in future work as a tool for clinicians and physiologists to measure the mechanical aspects of cardiovascular function outside of clinical settings, and to thereby titrate care for patients with CVDs, provide preventative screening, and optimize performance in austere environments by providing real-time in-depth information regarding performance and risk.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.