NavCog3

Sato, Daisuke; Oh, Uran; Naito, Kakuya; Takagi, Hironobu; Kitani, Kris M.; Asakawa, Chieko

doi:10.1145/3132525.3132535

Cited by 121 publications

(24 citation statements)

References 27 publications

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“…Most present systems use audio to deliver turn-by-turn directional instructions [5,11,18]. However, the human brain has its own understanding for positioning, direction, and velocity [19,20], which is not robot-styled.…”

Section: Human-machine Interactionmentioning

confidence: 99%

An ARCore Based User Centric Assistive Navigation System for Visually Impaired People

et al. 2019

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In this work, we propose an assistive navigation system for visually impaired people (ANSVIP) that takes advantage of ARCore to acquire robust computer vision-based localization. To complete the system, we propose adaptive artificial potential field (AAPF) path planning that considers both efficiency and safety. We also propose a dual-channel human–machine interaction mechanism, which delivers accurate and continuous directional micro-instruction via a haptic interface and macro-long-term planning and situational awareness via audio. Our system user-centrically incorporates haptic interfaces to provide fluent and continuous guidance superior to the conventional turn-by-turn audio-guiding method; moreover, the continuous guidance makes the path under complete control in avoiding obstacles and risky places. The system prototype is implemented with full functionality. Unit tests and simulations are conducted to evaluate the localization, path planning, and human–machine interactions, and the results show that the proposed solutions are superior to those of the present state-of-the-art solutions. Finally, integrated tests are carried out with low-vision and blind subjects to verify the proposed system.

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Section: Human-machine Interactionmentioning

confidence: 99%

An ARCore Based User Centric Assistive Navigation System for Visually Impaired People

et al. 2019

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“…There are several GPS-based navigation systems for outdoor environments designed for visually impaired people [8,22,34]. Although widespread solutions are still not a reality indoors, there are several eforts to support indoor localization and blind navigation assistance [17,57]. There is an increasing tendency for approaches where users do not require any hardware besides their own devices, for instance by making use of smartphone sensors [16,21].…”

Section: Indoor Navigation Assistancementioning

confidence: 99%

“…Other approaches use sensor installations in the environment, such as Wi-Fi [11,30], BLE beacons [40,57], or both [36]. BLE technology is now a popular approach to support blind navigation and has been implemented in universities [47], shopping malls [57], airports [7,33,37], among others. For instance, NavCog provides turn-by-turn instructions, showing an average localization error below 1.65 meters [57].…”

Section: Indoor Navigation Assistancementioning

confidence: 99%

“…Currently, several airports are equipped with location-based technologies aiming to revolutionize user's experience, including navigation assistance. Still, most eforts are focused on improving sighted people's experience, as the navigation does not consider the needs for higher accuracy and specifc navigation instructions of people with visual impairments [50,57,63]. Although valuable exceptions target navigation for visually impaired people [7,33,37], there is very little knowledge about their implementations and an absence of known evaluations to understand their efectiveness.…”

Section: Introductionmentioning

confidence: 99%

“…To fll this gap, we implemented a state-of-the-art indoor navigation system for visually impaired people at the Pittsburgh International Airport (PIT), aimed at supporting independent mobility. We extended an open source project (HULOP [31]) by adapting a smartphone-based navigation app called NavCog [57], which uses Bluetooth Low Energy (BLE) beacons for highly accurate indoor localization. In order to assess its impact, we conducted a real-world user study at the airport where ten visually impaired users navigated four relevant routes for their air travel experience.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Airport Accessibility and Navigation Assistance for People with Visual Impairments

Guerreiro

Ahmetovic

Sato

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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People with visual impairments often have to rely on the assistance of sighted guides in airports, which prevents them from having an independent travel experience. In order to learn about their perspectives on current airport accessibility, we conducted two focus groups that discussed their needs and experiences in-depth, as well as the potential role of assistive technologies. We found that independent navigation is a main challenge and severely impacts their overall experience. As a result, we equipped an airport with a Bluetooth Low Energy (BLE) beacon-based navigation system and performed a real-world study where users navigated routes relevant for their travel experience. We found that despite the challenging environment participants were able to complete their itinerary independently, presenting none to few navigation errors and reasonable timings. This study presents the frst systematic evaluation posing BLE technology as a strong approach to increase the independence of visually impaired people in airports. CCS CONCEPTS • Human-centered computing → Empirical studies in accessibility; Accessibility technologies.

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Research Challenges and Future Directions in Applying Cognitive Computing in the Healthcare Domain

Alluri

2022

Cognitive Intelligence and Big Data in Healthcare

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NavCog3

Cited by 121 publications

References 27 publications

An ARCore Based User Centric Assistive Navigation System for Visually Impaired People

An ARCore Based User Centric Assistive Navigation System for Visually Impaired People

Airport Accessibility and Navigation Assistance for People with Visual Impairments

Research Challenges and Future Directions in Applying Cognitive Computing in the Healthcare Domain

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