Bineeth Kuriakose

• Kuriakose, Bineeth; Shrestha, Raju & Sandnes, Frode Eika (2023). Exploring the User Experience of an AI-based Smartphone Navigation Assistant for People with Visual Impairments. I Gena, Cristina; Russis, Luigi De; Spano, Davide; Lanzilotti, Rosa; Mascio, Tania Di; Prandi, Catia & Andolina, Salvatore (Red.), CHItaly '23: Proceedings of the 15th Biannual Conference of the Italian SIGCHI Chapter. Association for Computing Machinery (ACM). ISSN 979-8-4007-0806-0. s. 1–8. doi: https:/doi.org/10.1145/3605390.3605421. Vis sammendrag

  Advancements in artificial intelligence (AI), computer vision, and smartphone technology have spurred the development of navigation assistants aiming to improve the independent mobility experience of people with visual impairments. Still, few studies have explored whether such tools meet this intention. Therefore, this study aimed to examine the perceptions of people with visual impairments on an AI-based navigation assistant and how their perceptions may influence their use of these technologies. A study involving 13 participants was conducted with DeepNAVI, an AI-based smartphone navigation assistant for people with visual impairments in both indoor and outdoor environments. The participants reported mixed reactions towards the navigation assistant, from excitement about the promise of increased independence to a lack of trust and skepticism toward the reliability of this technology. The perceptions from the participants provide some valuable insights about AI-based smartphone navigation assistants and their practicality in real life.

• Kuriakose, Bineeth; Ness, Ida Marie; Tengstedt, Maja Åskov; Svendsen, Jannicke Merete; Bjørseth, Therese & Pradhan, Bijay Lal [Vis alle 7 forfattere av denne artikkelen] (2023). Turn Left Turn Right - Delving type and modality of instructions in navigation assistant systems for people with visual impairments. International Journal of Human-Computer Studies. ISSN 1071-5819. 179. doi: 10.1016/j.ijhcs.2023.103098.
• Kuriakose, Bineeth; Cosescu, Irina; Shrestha, Raju & Sandnes, Frode Eika (2023). Mind Your Step: A Diary Study of a Person with Visual Impairment Navigating a City with a Smart Cane. I Papadopoulos, G.A.; Achilleos, A.; Pissaloux, E & Velazquez, R. (Red.), ICT for Health, Accessibility and Wellbeing. IHAW 2022. Springer. ISSN 978-3-031-29548-5. s. 34–48. doi: https:/doi.org/10.1007/978-3-031-29548-5_3.
• Kuriakose, Bineeth; Shrestha, Raju & Sandnes, Frode Eika (2022). DeepNAVI: A deep learning based smartphone navigation assistant for people with visual impairments. Expert Systems With Applications. ISSN 0957-4174. 212. doi: 10.1016/j.eswa.2022.118720. Vis sammendrag

  Navigation assistance is an active research area, where one aim is to foster independent living for people with vision impairments. Despite the fact that many navigation assistants use advanced technologies and methods, we found that they did not explicitly address two essential requirements in a navigation assistant - portability and convenience. It is equally imperative in designing a navigation assistant for the visually impaired that the device is portable and convenient to use without much training. Some navigation assistants do not provide users with detailed information about the obstacle types that can be detected, which is essential to make informed decisions when navigating in real-time. To address these gaps, we propose DeepNAVI, a smartphone-based navigation assistant that leverages deep learning competence. Besides providing information about the type of obstacles present, our system can also provide information about their position, distance from the user, motion status, and scene information. All this information is offered to users through audio mode without compromising portability and convenience. With a small model size and rapid inference time, our navigation assistant can be deployed on a portable device such as a smartphone and work seamlessly in a real-time environment. We conducted a pilot test with a user to assess the usefulness and practicality of the system. Our testing results indicate that our system has the potential to be a practical and useful navigation assistant for the visually impaired.

• Kuriakose, Bineeth; Shrestha, Raju & Sandnes, Frode Eika (2022). LiDAR-Based Obstacle Detection and Distance Estimation in Navigation Assistance for Visually Impaired. I Antona, Margherita & Stephanidis, Constantine (Red.), Universal Access in Human-Computer Interaction. User and Context Diversity. Springer. ISSN 978-3-031-05038-1. s. 479–491. doi: https:/doi.org/10.1007/978-3-031-05039-8_35. Vis sammendrag

  People with visual impairments can face challenges with independent navigation and therefore may use traditional aids such as guide dogs, white canes, or a travel companion for navigation assistance. In recent years, researchers have been working on AI-based navigation assistance systems. Obstacle detection and distance estimation are two of the key challenges in such systems. In this paper, we describe a LiDAR-based obstacle detection and distance estimation technique. A lightweight deep learning-based model called EfficientDet-LiteV4 is used for obstacle detection, and a depth map from the LiDAR is used to estimate the distance to the obstacles. We have implemented and tested the approach with the LiDAR integrated into a Raspberry Pi4 board. The results show good accuracy in detecting the obstacles and in estimating distance.

• Kuriakose, Bineeth; Shrestha, Raju & Sandnes, Frode Eika (2021). Distance Estimation Methods for Smartphone-Based Navigation Support Systems. I Arai, Kohei (Red.), Intelligent Systems and Applications, Proceedings of the 2021 Intelligent Systems Conference (IntelliSys 2021) Volume 1. Springer Nature. ISSN 978-3-030-82195-1. s. 658–673. doi: 10.1007/978-3-030-82196-8_49. Vis sammendrag

  Distance estimation is a key element of a navigation system. Various methods and instruments are used in distance estimation procedures. The methods and instruments used usually depend on the contexts of the application area. This paper compares the accuracy of five practical distance estimation methods that can be used on portable devices. Some of the methods selected for this study have currently not yet been used in the context of navigation systems. The experimental results show that Rule 57 and AR based distance estimation methods hold great potential for the practical application of navigation support as they provide adequate accuracy and computational efficiency.

• Kuriakose, Bineeth; Shrestha, Raju & Sandnes, Frode Eika (2021). Towards Independent Navigation with Visual Impairment: A Prototype of a Deep Learning and Smartphone-based Assistant. I Makedon, Fillia (Red.), PETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference. Association for Computing Machinery (ACM). ISSN 978-1-4503-8792-7. s. 113–114. doi: https:/doi.org/10.1145/3453892.3464946.
• Kuriakose, Bineeth; Shrestha, Raju & Sandnes, Frode Eika (2020). Multimodal Navigation Systems for Users with Visual Impairments—A Review and Analysis. Multimodal Technologies and Interaction. ISSN 2414-4088. 4(73). doi: 10.3390/mti4040073.
• Kuriakose, Bineeth; Shrestha, Raju & Sandnes, Frode Eika (2020). Tools and technologies for blind and visually impaired navigation support: A review. IETE Technical Review. ISSN 0256-4602. s. 1–17. doi: 10.1080/02564602.2020.1819893.
• Kuriakose, Bineeth; Shrestha, Raju & Sandnes, Frode Eika (2020). Smartphone Navigation Support for Blind and Visually Impaired People - A Comprehensive Analysis of Potentials and Opportunities. I Antona, M. & Stepahnidis, C. (Red.), Universal Access in Human-Computer Interaction, Applications and Practice. Springer Nature. ISSN 978-3-030-49107-9. s. 568–583. doi: https:/doi.org/10.1007/978-3-030-49108-6_41.

Se alle arbeider i Cristin

• Kuriakose, Bineeth; Shrestha, Raju & Sandnes, Frode Eika (2021). SceneRecog: A Deep Learning Scene Recognition Model for Assisting Blind and Visually Impaired Navigate using Smartphones. IEEE (Institute of Electrical and Electronics Engineers). ISBN 978-1-6654-4207-7. 3510 s.

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