ArticleBiosensors2022
An Artificial Intelligence-Enhanced Blood Pressure Monitor Wristband Based on Piezoelectric Nanogenerator.
Article in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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.
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Who cites it
16 citing papers in PubMed, 65 citations in OpenAlex.
- Article
- Wearable Sensors for Health Monitoring.Biosensors · 2026Review
- 3D Printing Assisted Wearable and Implantable Biosensors.Biosensors · 2025Review
- Applications of flexible materials in health management assisted by machine learning.RSC advances · 2025Review
- High-reward, high-risk technologies? An ethical and legal account of AI development in healthcare.BMC medical ethics · 2025Review
- Recent developments in machine learning modeling methods for hypertension treatment.Hypertension research : official journal of the Japanese Society of Hypertension · 2024Review
- From electricity to vitality: the emerging use of piezoelectric materials in tissue regeneration.Burns & trauma · 2024Review
- The 3D Printing of Nanocomposites for Wearable Biosensors: Recent Advances, Challenges, and Prospects.Bioengineering (Basel, Switzerland) · 2023Review
- Recent development of piezoelectric biosensors for physiological signal detection and machine learning assisted cardiovascular disease diagnosis.RSC advances · 2023Review
- Advancements in Passive Wireless Sensors, Materials, Devices, and Applications.Sensors (Basel, Switzerland) · 2023Review
- Review
- Combining Gaussian Process with Hybrid Optimal Feature Decision in Cuffless Blood Pressure Estimation.Diagnostics (Basel, Switzerland) · 2023Article
- Self-Powered Biosensors for Monitoring Human Physiological Changes.Biosensors · 2023Review
- Smart Mask as Wearable for Post-Pandemic Personal Healthcare.Biosensors · 2023Review
- Review
- Recent Progress of Nanogenerators for Green Energy Harvesting: Performance, Applications, and Challenges.Nanomaterials (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Hypertensive patients account for about 16% to 37% of the global population, and about 9.4 million people die each year from hypertension and its complications. Blood pressure is an important indicator for diagnosing hypertension. Currently, blood pressure measurement methods are mainly based on mercury sphygmomanometers in hospitals or electronic sphygmomanometers at home. However, people's blood pressure changes with time, and using only the blood pressure value at the current moment to judge hypertension may cause misdiagnosis. Continuous blood pressure measurement can monitor sudden increases in blood pressure, and can also provide physicians with long-term continuous blood pressure changes as a diagnostic reference. In this article, we design an artificial intelligence-enhanced blood pressure monitoring wristband. The wristband's sensors are based on piezoelectric nanogenerators, with a high signal-to-noise ratio of 29.7 dB. Through the transformer deep learning model, the wristband can predict blood pressure readings, and the loss value is lower than 4 mmHg. By wearing this blood pressure monitoring wristband, we realized three days of continuous blood pressure monitoring of the subjects. The blood pressure monitoring wristband is lightweight, has profound significance for the prevention and treatment of hypertension, and has wide application prospects in medical, military, aerospace and other fields.
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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.