Evidence map›Paper›PMID 40648236›Full record

ReviewSensors (Basel, Switzerland)2025

Bionic Sensors for Biometric Acquisition and Monitoring: Challenges and Opportunities.

Haoran Yu, Mingqi Ma, Baishun Zhang, Anxin Wang, Gaowei Zhong, Ziyuan Zhou, Chengxin Liu, Chunqing Li, Jingjing Fang, Yanbo He and 5 more

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Haoran YuSchool of Integrated Circuits, Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, China.
Mingqi MaSchool of Integrated Circuits, Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, China.
Baishun ZhangSchool of Integrated Circuits, Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, China.
Anxin WangSchool of Integrated Circuits, Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, China.
Gaowei ZhongSchool of Integrated Circuits, Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, China.
Ziyuan ZhouSchool of Integrated Circuits, Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, China.
Chengxin LiuSchool of Integrated Circuits, Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, China.
Chunqing LiSchool of Integrated Circuits, Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, China.
Jingjing FangSchool of Integrated Circuits, Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, China.
Yanbo HeSchool of Integrated Circuits, Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, China.
Donghai RenHuaibei Zhongtai Electromechanical Engineering Co. Ltd., Huaibei 235047, China.
Feifei DengSchool of Integrated Circuits, Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, China.
Qi HongSchool of Integrated Circuits, Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, China.
Yunong ZhaoSchool of Integrated Circuits, Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, China.ORCID 0000-0003-2439-8669
Xiaohui GuoSchool of Integrated Circuits, Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, China.

Funding

National Natural Science Foundation of China 61901005National Natural Science Foundation of China 62401004the Anhui Provincial Natural Science Foundation 2308085MF192the China Postdoctoral Science Foundation-Anhui Joint Support Program 2024T011AHthe University Synergy Innovation Program of Anhui Province GXXT-2023-075the University Synergy Innovation Program of Anhui Province GXXT-2023-106
6 · The paper itself

Abstract

The development of materials science, artificial intelligence and wearable technology has created both opportunities and challenges for the next generation of bionic sensor technology. Bionic sensors are extensively utilized in the collection and monitoring of human biological signals. Human biological signals refer to the parameters generated inside or outside the human body to transmit information. In a broad sense, they include bioelectrical signals, biomechanical information, biomolecules, and chemical molecules. This paper systematically reviews recent advances in bionic sensors in the field of biometric acquisition and monitoring, focusing on four major technical directions: bioelectric signal sensors (electrocardiograph (ECG), electroencephalograph (EEG), electromyography (EMG)), biomarker sensors (small molecules, large molecules, and complex-state biomarkers), biomechanical sensors, and multimodal integrated sensors. These breakthroughs have driven innovations in medical diagnosis, human-computer interaction, wearable devices, and other fields. This article provides an overview of the above biomimetic sensors and outlines the future development trends in this field.

Indexed as

BiometryBionicsBiosensing TechniquesElectrocardiographyElectroencephalographyElectromyographyHumansMonitoring, PhysiologicWearable Electronic Devicesbioelectric signal sensorsbiomarker sensorsbiomechanical sensorsbionic sensorsmultimodal integrated sensorswearable devices

Identifiers

PMID40648236
PMCPMC12252035

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.