Haptic feedback interface in navigation systems for visually impaired and blind people: a systematic review

Preprint 2021

Bei Xia and Youngjun Cho

Background: It is often challenging for visually impaired people to navigate independently. To help this, an increasing number of studies have explored navigation systems for visually impaired users. Particularly, the ways that the users communicate with such systems through touch have been actively studied. In this paper, we aim: (1) to systematically synthesise the development of haptic feedback interfaces for navigation systems for the visually impaired community; (2) to review evaluation approaches that have been used in this field.
Method: A systematic search was conducted in the following databases: PubMed, IEEE Xplore, ACM and ScienceDirect. Through our initial screening on titles and abstracts, a total of 94 articles were initially selected from the databases. Then our full-text review resulted in 32 articles for in-depth analysis.
Results: Three key themes emerged in haptic feedback-enabled navigation systems: i) obstacle avoidance, ii) direction instruction and iii) cognitive mapping. Vibro-tactile feedback is the most frequently used for directing navigation information, communicating through specific body areas such as hand, abdomen, arms, feet and back. The feedback type and design was dominantly influenced by the body part chosen for interacting with a system. Completion time was most widely adopted as a dependant variable in evaluation while accuracy-based metrics were often overlooked. Numerical metrics were often complemented by post-study user feedback surveys and interviews.
Conclusions: This review summarises the scientific literature on haptic feedback for navigation, which helps develop our understanding of the design trend in the field and contribute future directions. We believe attending more to the target user groups from the visually impaired community will significantly benefit, obtaining hidden insights and in turn driving innovation.
This work builds on our previous projects [1-4].


[1] Cho, Y., Bianchi, A., Marquardt, N. and Bianchi-Berthouze, N., 2016. RealPen: Providing realism in handwriting tasks on touch surfaces using auditory-tactile feedback. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology, pp. 195-205.

[2] Cho, Y., Bianchi-Berthouze, N., Marquardt, N. and Julier, S.J., 2018. Deep thermal imaging: Proximate material type recognition in the wild through deep learning of spatial surface temperature patterns. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1-13.

[3] Cho, Y. and Joung, M., 2017. Display apparatus and method for operating the same. U.S. Patent 9,733,765.

[4] Cho, Y., Joung, M. and Kim, S., 2015. Device and method for generating vibrations. U.S. Patent Application 14/758,397.