Learning-Based Visual Acuity Test System with Pepper Robot for User Behavior Research

Tseng, Shih-Huan; Sun, Pengyan

doi:10.1007/978-3-031-06053-3_39

Cited by 1 publication

(1 citation statement)

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“…However, the reliance on button or keyboard inputs for signaling responses poses challenges, mainly because these inputs are less intuitive compared to verbal or gesture-based responses, potentially reducing the subjects' focus. Additionally, the shared usage of clinical equipment raises concerns regarding hygiene, necessitating enhanced maintenance efforts (Tseng & Sun, 2022).…”

Section: Touch-based Interactionmentioning

confidence: 99%

A Visual Acuity Assessment System Based on Static Gesture Recognition and Naive Bayes Classifier

Li,

Tong

2024

International Journal of Information Technologies and Systems Approach

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Visual Acuity (VA) assessment is crucial for early vision screening, yet traditional methods are manual and time-consuming. Despite the advancements in human-computer interaction (HCI), there is no existing system fully addresses accuracy, efficiency and adaptability. This study introduces an intelligent VA assessment system that combines MediaPipe-based static gesture recognition with a novel Naive Bayes Classifier (NBC)-based VA Thresholds Determination (VATD) scheme. This integration offers a non-contact, user-friendly approach for rapid and precise VA testing. The VATD scheme is designed to significantly reduce the number of test trials; thereby, substantially improving the efficiency. Experimental validation confirms the system's high accuracy (96.72%) within a ±0.1 deviation from standard methods, achieves a 68% reduction in test time compared to traditional methods, and offers a 27% efficiency improvement over ANN-based systems. This system promises to enhance VA assessments, particularly for children and adolescents, with its speed, accuracy, and broader applicability.

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Section: Touch-based Interactionmentioning

confidence: 99%