AngleSizer: Enhancing Spatial Scale Perception for the Visually Impaired with an Interactive Smartphone Assistant

Author:

Jing Xiaoqing1ORCID,Yu Chun2ORCID,Yue Kun3ORCID,Lu Liangyou1ORCID,Gao Nan1ORCID,Shi Weinan2ORCID,Zhang Mingshan1ORCID,Wang Ruolin4ORCID,Shi Yuanchun5ORCID

Affiliation:

1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

2. Department of Computer Science and Technology, Tsinghua University, Beijing, China and Key Laboratory of Pervasive Computing, Ministry of Education, Beijing, China

3. School of Software, Tsinghua University, Beijing, China

4. University of California, Los Angeles, Los Angeles, CA, USA

5. Department of Computer Science and Technology, Tsinghua University, Beijing, China and Qinghai University, Xining, China

Abstract

Spatial perception, particularly at small and medium scales, is an essential human sense but poses a significant challenge for the blind and visually impaired (BVI). Traditional learning methods for BVI individuals are often constrained by the limited availability of suitable learning environments and high associated costs. To tackle these barriers, we conducted comprehensive studies to delve into the real-world challenges faced by the BVI community. We have identified several key factors hindering their spatial perception, including the high social cost of seeking assistance, inefficient methods of information intake, cognitive and behavioral disconnects, and a lack of opportunities for hands-on exploration. As a result, we developed AngleSizer, an innovative teaching assistant that leverages smartphone technology. AngleSizer is designed to enable BVI individuals to use natural interaction gestures to try, feel, understand, and learn about sizes and angles effectively. This tool incorporates dual vibration-audio feedback, carefully crafted teaching processes, and specialized learning modules to enhance the learning experience. Extensive user experiments validated its efficacy and applicability with diverse abilities and visual conditions. Ultimately, our research not only expands the understanding of BVI behavioral patterns but also greatly improves their spatial perception capabilities, in a way that is both cost-effective and allows for independent learning.

Publisher

Association for Computing Machinery (ACM)

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