AccessLens: Auto-detecting Inaccessibility of Everyday Objects

TL;DR

AccessLens addresses the problem of contextual inaccessibility in everyday interfaces by introducing an end-to-end pipeline built on AccessDB and AccessReal for inaccessibility detection, and AccessMeta to map detections to 3D-printed augmentation designs. The system combines a detector trained on a novel 21 inaccessibility classes across 6 object types with a metadata-driven dictionary of augmentations, enabling low-cost, user-driven retrofitting of legacy objects. Through design studies, user and expert evaluations, and detector experiments, the work demonstrates improved awareness and practical pathways for implementing accessibility enhancements, while discussing challenges such as domain gaps, customization needs, and standard alignment. The work envisions scalable, participatory approaches to accessibility that extend beyond diagnosed disabilities, potentially fostering altruistic and community-driven improvements to indoor environments.

Abstract

In our increasingly diverse society, everyday physical interfaces often present barriers, impacting individuals across various contexts. This oversight, from small cabinet knobs to identical wall switches that can pose different contextual challenges, highlights an imperative need for solutions. Leveraging low-cost 3D-printed augmentations such as knob magnifiers and tactile labels seems promising, yet the process of discovering unrecognized barriers remains challenging because disability is context-dependent. We introduce AccessLens, an end-to-end system designed to identify inaccessible interfaces in daily objects, and recommend 3D-printable augmentations for accessibility enhancement. Our approach involves training a detector using the novel AccessDB dataset designed to automatically recognize 21 distinct Inaccessibility Classes (e.g., bar-small and round-rotate) within 6 common object categories (e.g., handle and knob). AccessMeta serves as a robust way to build a comprehensive dictionary linking these accessibility classes to open-source 3D augmentation designs. Experiments demonstrate our detector's performance in detecting inaccessible objects.

Figures (13)

• Figure 1: (a) A round knob's accessibility can be improved by (b) lever extension door_lever_extension while (c) a lever handle's accessibility is improved by an (d) arm extension door_arm_extension. Everyday objects portray different accessibility barriers to people under different contexts.
• Figure 2: AccessLens's target user scope compared to existing assistive technique works and general in-home modifications. AccessLens supports users with limited awareness but who can easily become disabled under various contexts.
• Figure 3: Examples of 3D assistive augmentations that belong to three categories, obtained from our in-the-wild survey with iterative affinity diagramming. Each design has a thing_id at the bottom, and the design page can be located at https://www.thingiverse.com/thing:thing_id. Examples show that various challenges, such as motor and sensory barriers, can present even for one object. 3D augmentations are actively used to address challenges without requiring total replacement.
• Figure 4: AccessLens prototype overview. AccessLens allows users to scan an uploaded photo (a), view the detected inaccessible objects (b), and upon a click of a detected object, browse through the available suggestions (c).
• Figure 5: (a) 3D augmentation recommendations are assessed by two sub-metrics of easy installation and low-cost solution. (b) Perceived helpfulness is assessed by inaccessible object recognition, understanding contexts, and solution retrieval.