In this work, we have incorporated an electronic travel aid (ETA) as a smartphone application that alerts fall risks to the visually impaired. The application detects negative obstacles, such as platform edges and stairs, and occlusion using a grid map including height information to estimate fall risk based on the distance from an area’s edge to the user, and the area ratio. Here, we describe a grid map correction method based on the surrounding conditions of each cell to avoid area misclassification. The smartphone application incorporating this correction method was verified in environments similar to station platforms by evaluating its usefulness, robustness against environmental changes, and stability as a smartphone application. The verification results showed that the correction method is, in fact, useful in actual environments and can be implemented as a smartphone application.

1. [1] N. S. Ahmad, N. L. Boon, and P. Goh, “Multi-Sensor Obstacle Detection System via Model-Based State-Feedback Control in Smart Cane Design for the Visually Challenged,” IEEE Access, Vol.6, pp. 64182-64192, 2018. https://doi.org/10.1109/ACCESS.2018.2878423
2. [2] J.-R. Rizzo, K. Conti, T. Thomas et al., “A new primary mobility tool for the visually impaired: A white cane–adaptive mobility device hybrid,” Assist. Technol., Vol.30, No.5, pp. 219-225, 2018. https://doi.org/10.1080/10400435.2017.1312634
3. [3] R. K. Katzschmann, B. Araki, and D. Rus, “Safe Local Navigation for Visually Impaired Users with a Time-of-Flight and Haptic Feedback Device,” IEEE Trans. Neural Syst. Rehabil. Eng., Vol.26, No.3, pp. 583-593, 2018. https://doi.org/10.1109/TNSRE.2018.2800665
4. [4] E. J. A. Prada and L. M. S. Forero, “A belt-like assistive device for visually impaired people: Toward a more collaborative approach,” Cogent Engineering, Vol.9, No.1, Article No.2048440, 2022. https://doi.org/10.1080/23311916.2022.2048440
5. [5] C. Feltner, J. Guilbe, S. Zehtabian et al., “Smart Walker for the Visually Impaired,” 2019 IEEE Int. Conf. on Communications (ICC 2019), pp. 1-6, 2019. https://doi.org/10.1109/ICC.2019.8762081
6. [6] K. Tobita, K. Sagayama, M. Mori, and A. Tabuchi, “Structure and Examiation of the Guidance Robot LIGHBOT for Visually Impaired and Elderly People,” J. Robot. Mechatron., Vol.30, No.1, pp. 86-92, 2018. https://doi.org/10.20965/jrm.2018.p0086
7. [7] S. Alghamdi, R. v. Schyndel, and I. Khalil, “Accurate positioning using long range active RFID technology to assist visually impaired people,” J. of Network and Computer Applications, Vol.41, pp. 135-147, 2014. https://doi.org/10.1016/j.jnca.2013.10.015
8. [8] D. Sato, U. Oh, J. Guerreiro et al., “NavCog3 in the Wild: Large-scale Blind Indoor Navigation Assistant with Semantic Features,” ACM Trans. Access. Comput., Vol.12, No.3, Article No.14, 2019. https://doi.org/10.1145/3340319
9. [9] M. Kuribayashi, S. Kayukawa, H. Takagi et al., “LineChaser: A Smartphone-Based Navigation System for Blind People to Stand in Lines,” Proc. of the 2021 CHI Conf. on Human Factors in Computing Systems, Article No.33, 2021. https://doi.org/10.1145/3411764.3445451
10. [10] M. Kuribayashi, S. Kayukawa, J. Vongkulbhisal et al., “Corridor-Walker: Mobile Indoor Walking Assistance for Blind People to Avoid Obstacles and Recognize Intersections,” Proc. ACM Hum.-Comput. Interact., Vol.6, No.MHCI, Article No.179, 2022. https://doi.org/10.1145/3546714
11. [11] N. D. T. Aldas, S. Lee, C. Lee et al., “AIGuide: An Augmented Reality Hand Guidance Application for People with Visual Impairments,” The 22nd Int. ACM SIGACCESS Conf. on Computers and Accessibility, Article No.2, 2020. https://doi.org/10.1145/3373625.3417028
12. [12] Z. Zhong, Z. Wang, L. Lin et al., “Robust Negative Obstacle Detection in Off-Road Environments Using Multiple LiDARs,” 2020 6th Int. Conf. on Control, Automation and Robotics (ICCAR), pp. 700-705, 2020. https://doi.org/10.1109/ICCAR49639.2020.9108058
13. [13] L. Chen, J. Yang, and H. Kong, “Lidar-histogram for fast road and obstacle detection,” 2017 IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 1343-1348, 2017. https://doi.org/10.1109/ICRA.2017.7989159
14. [14] F. Xu, L. Chen, J. Lou, and M. Ren, “A real-time road detection method based on reorganized lidar data,” PLoS One, Vol.14, No.4, Article No.e0215159, 2019. https://doi.org/10.1371/journal.pone.0215159
15. [15] R. D. Morton and E. Olson, “Positive and negative obstacle detection using the HLD classifier,” 2011 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 1579-1584, 2011. https://doi.org/10.1109/IROS.2011.6095142
16. [16] T. Hines, K. Stepanas, F. Talbot et al., “Virtual Surfaces and Attitude Aware Planning and Behaviours for Negative Obstacle Navigation,” IEEE Robotics and Automation Letters, Vol.6, No.2, pp. 4048-4055, 2021. https://doi.org/10.1109/LRA.2021.3065302
17. [17] T. Overbye and S. Saripalli, “G-VOM: A GPU Accelerated Voxel Off-Road Mapping System,” 2022 IEEE Intelligent Vehicles Symp. (IV), pp. 1480-1486, 2022. https://doi.org/10.1109/IV51971.2022.9827107
18. [18] M. Bajracharya, J. Ma, M. Malchano et al., “High fidelity day/night stereo mapping with vegetation and negative obstacle detection for vision-in-the-loop walking,” 2013 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 3663-3670, 2013. https://doi.org/10.1109/IROS.2013.6696879
19. [19] D. Kim, D. Carballo, J. D. Calro et al., “Vision Aided Dynamic Exploration of Unstructured Terrain with a Small-Scale Quadruped Robot,” 2020 IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 2464-2470, 2020. https://doi.org/10.1109/ICRA40945.2020.9196777
20. [20] F. Jenelten, R. Grandia, F. Farshidian, and M. Hutter, “TAMOLS: Terrain-Aware Motion Optimization for Legged Systems,” IEEE Trans. Rob., Vol.38, No.6, pp. 3395-3413, 2022. https://doi.org/10.1109/TRO.2022.3186804
21. [21] T. Sasaki and T. Fujita, “Gap Traversing Motion via a Hexapod Tracked Mobile Robot Based on Gap Width Detection,” J. Robot. Mechatron., Vol.33, No.3, pp. 665-675, 2021. https://doi.org/10.20965/jrm.2021.p0665
22. [22] D. Katayama, K. Ishii, S. Yasukawa et al., “Fall Risk Estimation for Visually Impaired Using iPhone with LiDAR,” J. of Robotics, Networking and Artificial Life, Vol.9, No.4, pp. 349-357, 2023.
23. [23] Y. Wang, W.-L. Chao, D. Garg et al., “Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving,” Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 8437-8445, 2019. https://doi.org/10.1109/CVPR.2019.00864
24. [24] D. Ternes and K. E. MacLean, “Designing Large Sets of Haptic Icons with Rhythm,” Haptics: Perception, Devices and Scenarios, pp. 199-208, 2008. https://doi.org/10.1007/978-3-540-69057-3_24
25. [25] M. Tauchi and M. Ohkura, “A Present Situation and Problems of Independent Travel of Blind and Visual Impaired,” J. of the Society of Instrument and Control Engineers, Vol.34, No.2, pp. 140-146, 1995 (in Japanese). https://doi.org/10.11499/sicejl1962.34.140