The Human Touch: Using a Webcam to Autonomously Monitor Compliance During Visual Field Assessments

Pete R. Jones*, Giorgia Demaria, Iris Tigchelaar, Daniel S. Asfaw, David F. Edgar, Peter Campbell, Tamsin Callaghan, David P. Crabb

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

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    Purpose: To explore the feasibility of using various easy-to-obtain biomarkers to monitor non-compliance (measurement error) during visual field assessments.

    Methods: Forty-two healthy adults (42 eyes) and seven glaucoma patients (14 eyes) underwent two same-day visual field assessments. An ordinary webcam was used to compute seven potential biomarkers of task compliance, based primarily on eye gaze, head pose, and facial expression. We quantified the association between each biomarker and measurement error, as defined by (1) test-retest differences in overall test scores (mean sensitivity), and (2) failures to respond to visible stimuli on individual trials (stimuli -3 dB or more brighter than threshold).

    Results: In healthy eyes, three of the seven biomarkers were significantly associated with overall (test-retest) measurement error (P = 0.003-0.007), and at least two others exhibited possible trends (P = 0.052-0.060). The weighted linear sum of all seven biomarkers was associated with overall measurement error, in both healthy eyes (r = 0.51, P <0.001) and patients (r = 0.65, P <0.001). Five biomarkers were each associated with failures to respond to visible stimuli on individual trials (all P <0.001).

    Conclusions: Inexpensive, autonomous measures of task compliance are associated with measurement error in visual field assessments, in terms of both the overall reliability of a test and failures to respond on particular trials ("lapses"). This could be helpful for identifying low-quality assessments and for improving assessment techniques (e.g., by discounting suspect responses or by automatically triggering comfort breaks or encouragement).

    Translational Relevance: This study explores a potential way of improving the reliability of visual field assessments, a crucial but notoriously unreliable clinical measure.

    Original languageEnglish
    Article number31
    Pages (from-to)1-14
    Number of pages14
    JournalTranslational Vision Science & Technology
    Issue number8
    Publication statusPublished - Jul-2020


    • affective computing
    • visual fields
    • perimetry
    • glaucoma
    • compliance
    • adherence
    • vigilance
    • measurement error
    • reliability
    • psychophysics
    • eye gaze
    • head pose
    • facial expression
    • OpenFace
    • action units
    • machine learning
    • deep learning
    • computer vision
    • TIME

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