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Research PaperResearchia:202608.03040

A Biometric Sensor Network to Enable Real-Time Measurement of Individual Student Engagement in STEM Lecture Environments

Ahmed Elsayed

Abstract

Student engagement (SE) is a critical predictor of academic performance and retention in STEM education, yet existing measurement approaches are often intrusive, manually intensive, or unsuitable for real-time classroom use. This thesis proposes a novel $\textit{Biometric Sensor Network}$ (BSN) designed to enable real-time measurement and continuous tracking of individual student engagement in STEM classroom environments. The system enables capturing of behavioral, emotional, and cognitive indic...

Submitted: August 3, 2026Subjects: Engineering; Biomedical Engineering

Description / Details

Student engagement (SE) is a critical predictor of academic performance and retention in STEM education, yet existing measurement approaches are often intrusive, manually intensive, or unsuitable for real-time classroom use. This thesis proposes a novel Biometric Sensor Network\textit{Biometric Sensor Network} (BSN) designed to enable real-time measurement and continuous tracking of individual student engagement in STEM classroom environments. The system enables capturing of behavioral, emotional, and cognitive indicators through camera-based sensing while preserving ethical and privacy constraints. To measure these indicators unobtrusively and ethically, we propose a BSN composed of Student Processing Units\textit{Student Processing Units} (SPUs) that function as distributed sensing nodes. The network is explicitly designed to satisfy five objectives: it must be non-intrusive,non-invasive,non-stigmatizing,real-time\textbf{non-intrusive}, \textbf{non-invasive}, \textbf{non-stigmatizing}, \textbf{real-time}, and automatic\textbf{automatic}, while ensuring rigorous protection of student data security and privacy. Each SPU supports two operational modes: (i) a dataset-collection mode\textit{dataset-collection mode}, in which raw student video is temporarily recorded to construct a private SE dataset for model training and validation, and (ii) an analysis mode\textit{analysis mode}, in which the SPU performs real-time inference on 10-second video segments without storing or transmitting raw frames. In this analysis role, each SPU enables fully on-device processing---including face detection, gaze estimation, and affective analysis---ensuring that no identifiable video data leaves the device. A secure backend infrastructure manages device authentication, session orchestration, and encrypted data ingestion. The full system integrates hardware design, computer-vision pipelines, wireless networking, security protocols, and session-level data management.


Source: arXiv:2607.28944v1 - http://arxiv.org/abs/2607.28944v1 PDF: https://arxiv.org/pdf/2607.28944v1 Original Link: http://arxiv.org/abs/2607.28944v1

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Submission Info
Date:
Aug 3, 2026
Topic:
Biomedical Engineering
Area:
Engineering
Comments:
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