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Updated: Jul 4, 2026

Movement Retraining using Real-time Feedback of Performance

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

Operational integrity screening for telemedicine workflows: an explainable motion and audiovisual coherence

Karol Jędrasiak¹, Julia Bijoch²

¹WSB University, Dabrowa Gornicza, Poland.

Frontiers in Bioengineering and Biotechnology

|July 3, 2026

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This study introduces an AI-enabled integrity control for teleconsultations, detecting digital impersonation risks with high accuracy. The system uses motion dynamics and audiovisual coherence for reliable identity verification in healthcare.

Area of Science:

Biosecurity and Digital Health
Artificial Intelligence and Machine Learning
Cybersecurity and Data Integrity

Background:

Teleconsultations face AI-driven biosecurity risks from digital impersonation and synthetic media attacks.
These attacks can compromise identity, articulation, and consent evidence in digital healthcare.
Existing methods lack robust defenses against sophisticated, AI-enabled threats in telemedicine.

Purpose of the Study:

To evaluate an explainable integrity control for detecting AI-enabled biosecurity risks in teleconsultations.
To develop a system with extremely low false alarm rates and auditable evidence reporting.
To advance a calibrated, deployment-oriented integrity control architecture for operational screening in telemedicine.

Main Methods:

Keywords:

audiovisual synchrony biosecurity digital impersonation explainable AI healthcare governance motion dynamics patient safety telemedicine

Related Experiment Videos

Last Updated: Jul 4, 2026

Movement Retraining using Real-time Feedback of Performance

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

Developed an integrity control based on motion dynamics and audiovisual temporal coherence.

Utilized a reproducibility-oriented evaluation protocol and an evidence atlas of temporally grounded cues.

Employed staged fusion for scalable prescreening and targeted verification on the DeepFake RealWorld (DFRW) dataset, including systematically degraded variants.

Main Results:

Descriptor fusion achieved an Area Under the Curve (AUC) of 0.91.
A true positive rate (TPR) of 18.5% was reached at a false positive rate (FPR) of 0.1%.
Outperformed an Xception baseline (6.2% TPR at 0.1% FPR) on the DFRW dataset.

Conclusions:

The proposed integrity control demonstrates high efficacy in detecting synthetic media attacks in teleconsultations.
A two-stage deployment with explicit abstention and structured integrity reports is motivated by microbenchmarked latencies.
Broader validation across diverse demographics and clinical conditions is necessary before widespread clinical deployment.