Crowdsourcing of Accessibility Atributes in Urban Environment by Visually Impaired Pedestrians

Jakub Berka Jan Balata berkajak@fel.cvut.cz balatjan@fel.cvut.cz Czech Technical University in Prague Czech Technical University in Prague Faculty of Electrical Engineering Faculty of Electrical Engineering Prague, Czech Republic Prague, Czech Republic

Zdenek Mikovec xmikovec@fel.cvut.cz Czech Technical University in Prague Faculty of Electrical Engineering Prague, Czech Republic

ABSTRACT

Crowdsourcing of accessibility atributes for navigation of pedestrians with visual impairments is ofen focused on sighted crowd workers. In this position paper, we present preliminary results from the user study on crowdsourcing of accessibility atributes by visually impaired pedestrians themselves. Utilizing geodatabase based on sidewalk network rather than street network enabled travelers with visual impairment to collect selected atributes. We propose possible improvements and future work based on conversational agent providing relevant guidance during the crowdsourcing.

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Definitionrequest

Data  validation

Instruction request

Preference change

Attribute data

User

Definition

Navigation instruction

Attribute  request

Conversation agent

Route context

Segment

FeaturesFilled

Empty

Validation request

Figure 1: The the model of the conversa-tional agent for crowdsourcing accessibil-ity features on sidewalk based GIS.

Crowdsourcing of Accessibility Atributes in Urban Environment by Visually Impaired Pedestrians CHI 2019, May 04–09, 2019, Glasgow

KEYWORDS

crowdsourcing, visually impaired, accessibility, pedestrian navigation

INTRODUCTION

Navigation systems based on common geographical information system (GIS) build on street networks (i.e., Google Maps, TomTom, etc.) lack the information needed for comfortable traveling of blind pedestrians – accessibility atributes for safe route planning and providing a verbal description of the route in necessary detail.

In our previous work we designed a sidewalk-based GIS containing: line features tied to a large part of the pedestrian segment (sidewalk, crosswalk, underpass) with accessibility atributes (sidewalk slope, material, light signalization, trafic noise); and point features tied to short part of the pedestrian segment that acts as a barrier (staircase, obstacle) or a landmark (corner, recess) with atributes (corner shape, number of steps, unobstructed width). These features and their atributes were carefully designed and selected in cooperation with orientation and mobility specialists. This GIS enabled us, to generate landmark-enhanced itineraries for blind pedestrians [2] (see Fig. 3), and prototype a conversational navigation agent [1].

The GIS is created in two phases: 1) Pedestrian segments with line and point features are drawn into the GIS by professional using resources such as satellite imaging, “a map of town utilities,” creating a template for the second phase. 2) The template is filled in with atributes assigned to the features via professional “on-site reconnaissance,” which is highly labor demanding. The first phase is essential for routing algorithms to work and should be created by professionals.

However, the on-site reconnaissance can be delegated to the crowd.

RELATED WORK

There are several projects and research papers exploring crowdsourcing of accessibility data by sighted volunteers using Google Street View to be used by visually impaired or wheelchair users, such as [4–6]. See et al. [7] compared crowdsourced geodata from experts and non-experts and concluded that there is no significant diference in quality, what more non-experts can improve with training and good examples. The crowd can provide not only the data but also validation [3]. However, sighted crowd workers may not see a clear benefit for them, in contrast to for example

crowdsourcing data in Waze or Google map reviews. For our work, we chose to look at the direct beneficiaries of the collected data – pedestrians with visual impairments.

Figure 2: Complex part of the experimen-tal route witch numerous pedestrian cross-ings and turns. Red line represents the route segments.

Crowdsourcing of Accessibility Atributes in Urban Environment by Visually Impaired Pedestrians CHI 2019, May 04–09, 2019, Glasgow

CROWDSOURCING BY VISUALLY IMPAIRED PEDESTRIANS

As the first step, we created a prototype of a navigation application (see Figures 3 and 4), which also asked the participant to collect the atributes during the route walkthrough.

We conducted a user study with 6 visually impaired participants. Our research question was whether the visually impaired pedestrians are able to collect correct data about the accessibility atributes in the real environment. The experimental route was approximately 700 meters long and consisted of 19 segments with 18 decision points. The route was selected to cover a higher amount of accessibility features (see part of the route in Fig. 2). Furthermore, we investigated the subjective judgment of the participants about the level of safety, comprehension of the questions and ofered answers, and willingness to collect the data in this way. Finally, we asked the participants for comments and opinions in semi-structured interviews. From our first user study, we learned that some of the accessibility atributes can be collected by

visually impaired with high quality (e.g., corners shape, crossing signalization, and side features). We also found out that some of the atributes are hard to collect correctly (e.g., sidewalk slopes, trafic type on the crossroad). From the results of the subjective evaluation questionnaire as well as from the subsequent semi-structured interview with the participants, we found out that most of them would be willing to collect data on the accessibility atributes of the urban environment in a similar way to this user study. The interviews also showed that the time they would be willing to spend on one question ranged

between 10 and 45 seconds. The number of questions they were willing to answer on a route of similar length difered widely among the participants, some were willing to answer up to 3 questions, others even 10 to 15 questions.

We also asked how the participants perceive the credibility of the information. Some participants prefer data gathered by the visually impaired to those by sighted on the other hand others said that crowdsourced data may be untrustworthy. Some of the participants were concerned that, for example, the slopes of the sidewalks can be perceived by each person diferently.

NEXT STEPS

With the advances in natural language understanding and text to speech, the new devices and services provide accessible interfaces for people with visual impairments without the necessity to interact with devices such as a smartphone with VoiceOver while traveling. In our previous work, we already explored the role of the conversational agents in the navigation of visually impaired [1]. We showed, how the conversation can be used for adjusting the level of detail in route itineraries or asking for points of interest nearby.

Figure 3: Landmark-enhanced navigation instruction from a prototype (translated from Czech).

Figure 4: Crowdsourcing question (i.e., accessibility atribute), with possible an-swers (i.e., allowed values) (translated from Czech).

Crowdsourcing of Accessibility Atributes in Urban Environment by Visually Impaired Pedestrians CHI 2019, May 04–09, 2019, Glasgow

Using the natural language conversation we can address the issues of understanding values for various atributes (such as “What does it mean ‘round’ corner?” ; see Excerpt 1, line 5), asking for more details of navigation instruction (line 11) or changing preferences (e.g., system will ask only for the atributes user is sure to recognize “Don’t ask me about crossings anymore!”), as requested by the participants in the study. Moreover, the agent can also ask for validation of already collected accessibility atributes (line 16).

Our aim is to design a prototype of a conversational agent (see Figure 1) that would enable visually impaired pedestrians to collect new atributes for the features like sidewalk slope and material, corner shape, crosswalk curbs, ramps, and signalization, furthermore report temporary obstacles (potholes, construction works, restaurant gardens) and also validate other crowdsourced accessibility atributes. Moreover, the atributes collected by visually impaired people may build more trust in the crowdsourced atributes from others in the community of visually impaired and also motivate them to travel more.

CONCLUSION

In this position paper, we showed the preliminary results from the user study on crowdsourcing the accessibility atributes of environment features by visually impaired pedestrians. We found out that their atitude towards crowdsourcing is rather positive and may also serve as a motivation for independent traveling. Similarly, the quality of the collected data itself reached a satisfactory level. For the future work, we propose to design a conversational agent, to provide richer hands-free

interaction while maintaining the accessibility. The agent would enable users to collect accessibility atributes, to provide them with guidance and explanation for collection, and to intuitively adjust how much data are required to collect given their time constraints and individual skills and abilities.

Acknowledgements. This research was supported by project CZ.02.1.01/0.0/0.0/16_019/0000765 “Re-search Center for Informatics” funded by the OP VVV MEYS; project “Navigation of handicapped people” funded by grant no. SGS16/236/OHK3/3T/13; and by project “Integration the services for route planning and navigation system for the disabled with the city management systems and open data of cities” TH03010447 funded by Technological Agency of Czech Republic.

Excerpt 1: Example of the dialog between the user (U) and the conversational agent (A) during crowdsourcing of accessibility atributes.

01 A: Turn to the left and go approximately 02 150 meters to the corner. 03 U: What is the shape of that corner? 04 A: There will be a round corner. 05 U: What is a round corner? 06 A: A round corner is an arc larger 07 than 2 meters 08 U: Okay, I am here. 09 A: Okay, I’ll tell you how to continue. 10 Continue straight and cross the street. 11 U: Can you tell me more details 12 about the crossing? 13 A: It is a crossing with tactile 14 and sound signalization. 15 U: Okay, I am on the other side. 16 A: Can you confirm please if the crossing 17 has tactile and sound signalization? 18 U: Yes, it has.

REFERENCES [1] Jan Balata, Zdenek Mikovec, and Pavel Slavik. 2018. Conversational Agents for Physical World Navigation. In Studies in

Conversational UX Design. Springer, 61–83. [2] Jan Balata, Zdenek Mikovec, and Pavel Slavik. 2018. Landmark-enhanced route itineraries for navigation of blind pedestrians

in urban environment. Journal on Multimodal User Interfaces 12, 3 (2018), 181–198. [3] Michael F Goodchild and Linna Li. 2012. Assuring the quality of volunteered geographic information. Spatial statistics 1

(2012), 110–120.

Crowdsourcing of Accessibility Atributes in Urban Environment by Visually Impaired Pedestrians CHI 2019, May 04–09, 2019, Glasgow

[4] Richard Guy and Khai Truong. 2012. CrossingGuard: exploring information content in navigation aids for visually impaired pedestrians. In CHI 2012. ACM, 405–414.

[5] Kotaro Hara, Shiri Azenkot, Megan Campbell, Cynthia L Bennet, Vicki Le, Sean Pannella, Robert Moore, Kelly Minckler, Rochelle H Ng, and Jon E Froehlich. 2015. Improving public transit accessibility for blind riders by crowdsourcing bus stop landmark locations with google street view: An extended analysis. ACM Transactions on Accessible Computing (TACCESS) 6, 2 (2015), 5.

[6] Kotaro Hara, Vicki Le, and Jon Froehlich. 2013. Combining crowdsourcing and google street view to identify street-level accessibility problems. In CHI 2013. ACM, 631–640.

[7] Linda See, Alexis Comber, Carl Salk, Stefen Fritz, Marijn van der Velde, Christoph Perger, Christian Schill, Ian McCallum, Florian Kraxner, and Michael Obersteiner. 2013. Comparing the quality of crowdsourced data contributed by expert and non-experts. PloS one 8, 7 (2013), e69958.