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A digital signal processing approach for affective sensing of a computer user through pupil diameter monitoring.

机译：一种通过瞳孔直径监视来感测计算机用户的数字信号处理方法。

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摘要

Recent research has indicated that the pupil diameter (PD) in humans varies with their affective states. However, this signal has not been fully investigated for affective sensing purposes in human-computer interaction systems. This may be due to the dominant separate effect of the pupillary light reflex (PLR), which shrinks the pupil when light intensity increases.;In this dissertation, an adaptive interference canceller (AIC) system using the Hinfinity time-varying (HITV) adaptive algorithm was developed to minimize the impact of the PLR on the measured pupil diameter signal. The modified pupil diameter (MPD) signal, obtained from the AIC was expected to reflect primarily the pupillary affective responses (PAR) of the subject. Additional manipulations of the AIC output resulted in a processed MPD (PMPD) signal, from which a classification feature, PMPDmean, was extracted. This feature was used to train and test a support vector machine (SVM), for the identification of stress states in the subject from whom the pupil diameter signal was recorded, achieving an accuracy rate of 77.78%.;The advantages of affective recognition through the PD signal were verified by comparatively investigating the classification of stress and relaxation states through features derived from the simultaneously recorded galvanic skin response (GSR) and blood volume pulse (BVP) signals, with and without the PD feature. The discriminating potential of each individual feature extracted from GSR, BVP and PD was studied by analysis of its receiver operating characteristic (ROC) curve. The ROC curve found for the PMPDmean feature encompassed the largest area (0.8546) of all the single-feature ROCs investigated.;The encouraging results seen in affective sensing based on pupil diameter monitoring were obtained in spite of intermittent illumination increases purposely introduced during the experiments. Therefore, these results confirmed the benefits of using the AIC implementation with the HITV adaptive algorithm to isolate the PAR and the potential of using PD monitoring to sense the evolving affective states of a computer user.
机译：最近的研究表明，人的瞳孔直径（PD）随他们的情感状态而变化。但是，该信号尚未在人机交互系统中用于情感感测目的进行充分研究。这可能是由于瞳孔光反射（PLR）的主要独立作用，当光强度增加时，瞳孔光反射会收缩瞳孔。本文采用自适应时变（HITV）自适应技术的自适应干扰消除器（AIC）系统开发了一种算法，以最大程度地减少PLR对测得的瞳孔直径信号的影响。预期从AIC获得的修改后的瞳孔直径（MPD）信号主要反映受试者的瞳孔情感反应（PAR）。 AIC输出的其他操作导致处理的MPD（PMPD）信号，从中提取了分类特征PMPDmean。此功能用于训练和测试支持向量机（SVM），以识别记录了瞳孔直径信号的对象中的压力状态，从而达到了77.78％的准确率。通过从同时记录的皮肤电反应（GSR）和血容量脉冲（BVP）信号衍生的特征中比较研究压力和松弛状态的分类来验证PD信号，无论是否具有PD功能。通过分析其接收器工作特性（ROC）曲线，研究了从GSR，BVP和PD中提取的每个特征的识别潜力。发现的PMPDmean特征的ROC曲线涵盖了所研究的所有单特征ROC的最大面积（0.8546）.;尽管在实验过程中故意引入了间歇照明，但仍获得了基于瞳孔直径监测的情感感知中令人鼓舞的结果。因此，这些结果证实了将AIC实现与HITV自适应算法结合使用来隔离PAR的好处，以及使用PD监控来感测计算机用户不断发展的情感状态的潜力。

著录项

  • 作者

    Gao, Ying.;

  • 作者单位

    Florida International University.;

  • 授予单位 Florida International University.;
  • 学科 Engineering Computer.;Engineering Biomedical.;Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 170 p.
  • 总页数 170
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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