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Self-supervised Learning for Reading Activity Classification

Authors:

Md. Rabiul Islam,

Shuji Sakamoto,

Yoshihiro Yamada,

Andrew W. Vargo,

Masakazu Iwamura,

Koichi KiseAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 5, Issue 3

Article No.: 105, Pages 1 - 22

https://doi.org/10.1145/3478088

Published: 14 September 2021 Publication History

Abstract

Reading analysis can relay information about user's confidence and habits and can be used to construct useful feedback. A lack of labeled data inhibits the effective application of fully-supervised Deep Learning (DL) for automatic reading analysis. We propose a Self-supervised Learning (SSL) method for reading analysis. Previously, SSL has been effective in physical human activity recognition (HAR) tasks, but it has not been applied to cognitive HAR tasks like reading. We first evaluate the proposed method on a four-class classification task on reading detection using electrooculography datasets, followed by an evaluation of a two-class classification task of confidence estimation on multiple-choice questions using eye-tracking datasets. Fully-supervised DL and support vector machines (SVMs) are used as comparisons for the proposed SSL method. The results show that the proposed SSL method is superior to the fully-supervised DL and SVM for both tasks, especially when training data is scarce. This result indicates the proposed method is the superior choice for reading analysis tasks. These results are important for informing the design of automatic reading analysis platforms.

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Cited By

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https://dl.acm.org/doi/10.1145/3631422
Tripathi GSabherwal MSingh P(2024)“I know I have this till my Last Breath”: Unmasking the Gaps in Chronic Obstructive Pulmonary Disease (COPD) Care in IndiaProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642504(1-16)Online publication date: 11-May-2024
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Index Terms

Self-supervised Learning for Reading Activity Classification
1. Human-centered computing
  1. Human computer interaction (HCI)

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 5, Issue 3

Sept 2021

1443 pages

EISSN:2474-9567

DOI:10.1145/3486621

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Published: 14 September 2021

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Adhikary RSadeh MBatra NGoel M(2024)JoulesEyeProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314227:4(1-29)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3631422
Tripathi GSabherwal MSingh P(2024)“I know I have this till my Last Breath”: Unmasking the Gaps in Chronic Obstructive Pulmonary Disease (COPD) Care in IndiaProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642504(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642504
Baskaran PAdams J(2023)Multi-dimensional task recognition for human-robot teaming: literature reviewFrontiers in Robotics and AI10.3389/frobt.2023.112337410Online publication date: 7-Aug-2023
https://doi.org/10.3389/frobt.2023.1123374
Zhu RShi LSong YCai Z(2023)Integrating Gaze and Mouse Via Joint Cross-Attention Fusion Net for Students' Activity Recognition in E-learningProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36108767:3(1-35)Online publication date: 27-Sep-2023
https://doi.org/10.1145/3610876
Adhikary RLodhavia DFrancis CPatil RSrivastava TKhanna PBatra NBreda JPeplinski JPatel S(2023)SpiroMask: Measuring Lung Function Using Consumer-Grade MasksACM Transactions on Computing for Healthcare10.1145/35701674:1(1-34)Online publication date: 27-Feb-2023
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Baray SAhmed MChowdhury MKise K(2023)EOG-Based Reading Detection in the Wild Using Spectrograms and Nested Classification ApproachIEEE Access10.1109/ACCESS.2023.331603211(105619-105632)Online publication date: 2023
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Tarim EErimez BDegirmenci MCumhur Tekin H(2023)A Wearable Device Integrated with Deep Learning‐Based Algorithms for the Analysis of Breath PatternsAdvanced Intelligent Systems10.1002/aisy.2023001745:11Online publication date: 18-Aug-2023
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ISLAM MVARGO AIWATA MIWAMURA MKISE K(2022)Exploring Sensor Modalities to Capture User Behaviors for Reading DetectionIEICE Transactions on Information and Systems10.1587/transinf.2020ZDL0003E105.D:9(1629-1633)Online publication date: 1-Sep-2022
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Okadome YAta KIshiguro HNakamura Y(2022)Self-supervised Learning Method for Behavior Prediction during Dialogue Based on Temporal Consistency対話中の振る舞い予測のための時間的整合性に注目した自己教師あり学習Transactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.37-6_B-M4337:6(B-M43_1-13)Online publication date: 1-Nov-2022
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