Evidence map›Paper›PMID 41829523›Full record

ReviewSensors (Basel, Switzerland)2026

Artificial Intelligence-Enhanced Flexible Sensors for Human Motion and Posture Sensing.

Yiru Jiang, Tianyiyi He

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2026. 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

2 authors.

Yiru JiangArtificial Intelligence Research Institute, Shenzhen MSU-BIT University, Shenzhen 518100, China.
Tianyiyi HeArtificial Intelligence Research Institute, Shenzhen MSU-BIT University, Shenzhen 518100, China.

Funding

Engineering Technology Research Center for Ordinary Universities in Guangdong Province 2024GCZX005Key Field Projects of Ordinary Universities in Guangdotng Province 2025ZDZX1043National Natural Science Foundation of China 62401372National Natural Science Foundation of China 62576213Shenzhen Science and Technology Program JCYJ20250604173050029
6 · The paper itself

Abstract

In the era of Industry 4.0, artificial intelligence technology is experiencing rapid development, and the integration of artificial intelligence (AI) with flexible sensors has emerged as a transformative approach for human motion and posture sensing. This paper explores the advancements in AI-enhanced flexible sensors, focusing on the application of flexible sensors on various parts of the human body. Flexible sensors, due to their conformability and sensitivity, are ideal for capturing the dynamic and subtle movements of the human body. AI algorithms, particularly machine learning and deep learning techniques are employed to process the complex data streams from these sensors, enabling the accurate recognition and prediction of various human postures and motions. The combination of these technologies overcomes the limitations of traditional sensing systems, offering higher precision, adaptability, and real-time feedback. It can be applied to healthcare for rehabilitation monitoring, sports for performance enhancement, and human-computer interaction for intuitive control. This review also discusses the challenges such as sensor reliability, data privacy, and power management. The future outlook emphasizes more sophisticated AI models and deeper technology integration, promising a seamless integration into everyday life for enhanced human-machine interaction and health monitoring.

Indexed as

Artificial IntelligenceBiosensing TechniquesPostureAlgorithmsHumansIntelligent SystemsMachine LearningMonitoring, PhysiologicMotionMotion CaptureMovementSoft ComputingWearable Electronic DevicesAI-enhanced sensorsflexible sensorshuman motion monitoringintelligent wearable systemsself-powered sensors

Identifiers

PMID41829523
PMCPMC12986797

What OpenQuestion holds

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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 OpenQuestion graph.