Echo: A Human–Computer Collaborative Design of an Intelligent Object-Finding System for the Visually Impaired

Abstract

Locating everyday objects independently remains a persistent challenge for visually impaired individuals. Existing assistive tools often provide limited functionality, such as audio cues or object labeling, but rarely support both object recognition and retrieval. The key design challenge is developing an object-finding system that aligns with non-visual cognition and interaction habits.This study proposes Echo, an intelligent assistive system integrating AI-based visual recognition, RFID sensing, and multimodal feedback to support object localization and identification in home environments. The system adopts a human–computer collaborative framework consisting of two modules: semantic registration and real-time object finding. The interaction workflow follows three stages: fuzzy retrieval, dynamic guidance, and terminal confirmation. AI visual recognition identifies distinct objects and provides voice-guided navigation, while RFID tags enable rapid identification of visually similar items such as documents or medicine bottles.The research was conducted in three phases: user interviews and home observations (N = 12), prototype development, and usability evaluation with 20 visually impaired participants performing standardized object-finding tasks in a simulated home environment. Results indicate that Echo improved task efficiency and success rates. The tactile-dominant wristband interface with vibrotactile feedback reduced learning costs and supported spatial awareness during navigation.The findings highlight the value of human factors–oriented design in intelligent assistive technologies and demonstrate how human–computer collaboration can enhance autonomy for visually impaired users.

Keywords: Accessible Design, Assistive Design For The Visually Impaired, Intelligent Object-finding System

DOI: 10.54941/ahfe1007292