AI-based Multimodal Biometrics for Detecting Smartphone Distractions: Application to Online Learning

• URL: http://arxiv.org/abs/2506.17364v2
• Date: Tue, 24 Jun 2025 13:38:12 GMT
• Title: AI-based Multimodal Biometrics for Detecting Smartphone Distractions: Application to Online Learning
• Authors: Alvaro Becerra, Roberto Daza, Ruth Cobos, Aythami Morales, Mutlu Cukurova, Julian Fierrez,
• Abstract summary: We propose an AI-based approach that leverages physiological signals and head pose data to detect phone use.<n>Our results show that single biometric signals, such as brain waves or heart rate, offer limited accuracy, while head pose alone achieves 87%.<n>A multimodal model combining all signals reaches 91% accuracy, highlighting the benefits of integration.
• Score: 13.124145425838792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work investigates the use of multimodal biometrics to detect distractions caused by smartphone use during tasks that require sustained attention, with a focus on computer-based online learning. Although the methods are applicable to various domains, such as autonomous driving, we concentrate on the challenges learners face in maintaining engagement amid internal (e.g., motivation), system-related (e.g., course design) and contextual (e.g., smartphone use) factors. Traditional learning platforms often lack detailed behavioral data, but Multimodal Learning Analytics (MMLA) and biosensors provide new insights into learner attention. We propose an AI-based approach that leverages physiological signals and head pose data to detect phone use. Our results show that single biometric signals, such as brain waves or heart rate, offer limited accuracy, while head pose alone achieves 87%. A multimodal model combining all signals reaches 91% accuracy, highlighting the benefits of integration. We conclude by discussing the implications and limitations of deploying these models for real-time support in online learning environments.

Related papers

• DeepFace-Attention: Multimodal Face Biometrics for Attention Estimation with Application to e-Learning [18.36413246876648]
  This work introduces an innovative method for estimating attention levels (cognitive load) using an ensemble of facial analysis techniques applied to webcam videos. Our approach adapts state-of-the-art facial analysis technologies to quantify the users' cognitive load in the form of high or low attention. Our method outperforms existing state-of-the-art accuracies using the public mEBAL2 benchmark.
  arXiv  Detail & Related papers  (2024-08-10T11:39:11Z)
• MuJo: Multimodal Joint Feature Space Learning for Human Activity Recognition [2.7532797256542403]
  Human activity recognition (HAR) is a long-standing problem in artificial intelligence with applications in a broad range of areas.<n>We introduce our comprehensive Fitness Multimodal Activity dataset (FiMAD) to enhance HAR performance across various modalities.<n>We show that FiMAD can increase the performance on real HAR datasets such as MM-Fit, MyoGym, MotionSense, and MHEALTH.
  arXiv  Detail & Related papers  (2024-06-06T08:42:36Z)
• MultiIoT: Benchmarking Machine Learning for the Internet of Things [70.74131118309967]
  The next generation of machine learning systems must be adept at perceiving and interacting with the physical world. sensory data from motion, thermal, geolocation, depth, wireless signals, video, and audio are increasingly used to model the states of physical environments. Existing efforts are often specialized to a single sensory modality or prediction task. This paper proposes MultiIoT, the most expansive and unified IoT benchmark to date, encompassing over 1.15 million samples from 12 modalities and 8 real-world tasks.
  arXiv  Detail & Related papers  (2023-11-10T18:13:08Z)
• Multimodal Stress Detection Using Facial Landmarks and Biometric Signals [1.0124625066746595]
  Multi-modal learning aims to capitalize on the strength of each modality rather than relying on a single signal. This paper proposes a multi-modal learning approach for stress detection that integrates facial landmarks and biometric signals.
  arXiv  Detail & Related papers  (2023-11-06T23:20:30Z)
• Incremental procedural and sensorimotor learning in cognitive humanoid robots [52.77024349608834]
  This work presents a cognitive agent that can learn procedures incrementally. We show the cognitive functions required in each substage and how adding new functions helps address tasks previously unsolved by the agent. Results show that this approach is capable of solving complex tasks incrementally.
  arXiv  Detail & Related papers  (2023-04-30T22:51:31Z)
• A Convolutional Spiking Network for Gesture Recognition in Brain-Computer Interfaces [0.8122270502556371]
  We propose a simple yet efficient machine learning-based approach for the exemplary problem of hand gesture classification based on brain signals. We demonstrate that this approach generalizes to different subjects with both EEG and ECoG data and achieves superior accuracy in the range of 92.74-97.07%.
  arXiv  Detail & Related papers  (2023-04-21T16:23:40Z)
• Towards Continual Egocentric Activity Recognition: A Multi-modal Egocentric Activity Dataset for Continual Learning [21.68009790164824]
  We present a multi-modal egocentric activity dataset for continual learning named UESTC-MMEA-CL. It contains synchronized data of videos, accelerometers, and gyroscopes, for 32 types of daily activities, performed by 10 participants. Results of egocentric activity recognition are reported when using separately, and jointly, three modalities: RGB, acceleration, and gyroscope.
  arXiv  Detail & Related papers  (2023-01-26T04:32:00Z)
• MATT: Multimodal Attention Level Estimation for e-learning Platforms [16.407885871027887]
  This work presents a new multimodal system for remote attention level estimation based on multimodal face analysis. Our multimodal approach uses different parameters and signals obtained from the behavior and physiological processes that have been related to modeling cognitive load. The mEBAL database is used in the experimental framework, a public multi-modal database for attention level estimation obtained in an e-learning environment.
  arXiv  Detail & Related papers  (2023-01-22T18:18:20Z)
• Navigating to Objects in the Real World [76.1517654037993]
  We present a large-scale empirical study of semantic visual navigation methods comparing methods from classical, modular, and end-to-end learning approaches. We find that modular learning works well in the real world, attaining a 90% success rate. In contrast, end-to-end learning does not, dropping from 77% simulation to 23% real-world success rate due to a large image domain gap between simulation and reality.
  arXiv  Detail & Related papers  (2022-12-02T01:10:47Z)
• Mobile Behavioral Biometrics for Passive Authentication [65.94403066225384]
  This work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits. Experiments are performed over HuMIdb, one of the largest and most comprehensive freely available mobile user interaction databases. In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke.
  arXiv  Detail & Related papers  (2022-03-14T17:05:59Z)
• Federated Learning: A Signal Processing Perspective [144.63726413692876]
  Federated learning is an emerging machine learning paradigm for training models across multiple edge devices holding local datasets, without explicitly exchanging the data. This article provides a unified systematic framework for federated learning in a manner that encapsulates and highlights the main challenges that are natural to treat using signal processing tools.
  arXiv  Detail & Related papers  (2021-03-31T15:14:39Z)
• Anomaly Detection in Video via Self-Supervised and Multi-Task Learning [113.81927544121625]
  Anomaly detection in video is a challenging computer vision problem. In this paper, we approach anomalous event detection in video through self-supervised and multi-task learning at the object level.
  arXiv  Detail & Related papers  (2020-11-15T10:21:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.