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When Refreshable Tactile Displays Meet Conversational Agents: Investigating Accessible Data Presentation and Analysis with Touch and Speech

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arxiv 2408.04806 v2 pith:Z6EDWPEF submitted 2024-08-09 cs.HC

classification cs.HC
keywords datatactileanalysisparticipantspeopleconversationalaccessibleagents
verification ladder T0 review T1 audit T2 compute T3 formal
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Despite the recent surge of research efforts to make data visualizations accessible to people who are blind or have low vision (BLV), how to support BLV people's data analysis remains an important and challenging question. As refreshable tactile displays (RTDs) become cheaper and conversational agents continue to improve, their combination provides a promising approach to support BLV people's interactive data exploration and analysis. To understand how BLV people would use and react to a system combining an RTD with a conversational agent, we conducted a Wizard-of-Oz study with 11 BLV participants, where they interacted with line charts, bar charts, and isarithmic maps. Our analysis of participants' interactions led to the identification of nine distinct patterns. We also learned that the choice of modalities depended on the type of task and prior experience with tactile graphics, and that participants strongly preferred the combination of RTD and speech to a single modality. In addition, participants with more tactile experience described how tactile images facilitated a deeper engagement with the data and supported independent interpretation. Our findings will inform the design of interfaces for such interactive mixed-modality systems.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Accessible Data Access and Analysis by People who are Blind or Have Low Vision

    cs.HC 2025-06 conditional novelty 6.0 of 10

    A research agenda and preliminary study propose a multimodal system pairing tactile graphics with a conversational agent to support data analysis for blind and low vision users.

  2. OccluNet: Spatio-Temporal Deep Learning for Occlusion Detection on DSA

    cs.CV 2025-08 unverdicted novelty 5.0 of 10

    OccluNet, a spatio-temporal attention-based detector, is claimed to outperform frame-based YOLOv11 baselines for occlusion detection in DSA sequences.

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