Page 45 - Read Online

Page 194 Chen et al. Intell Robot 2024;4:179-95 I http://dx.doi.org/10.20517/ir.2024.11

8. Zhang K, Liu H, Fan Z, et al. Foot placement prediction for assistive walking by fusing sequential 3D gaze and environmental context.
IEEE Robot Autom Lett 2021;6:2509–16. DOI
9. Yang B, Huang J, Chen X, Xiong C, Hasegawa Y. Supernumerary robotic limbs: a review and future outlook. IEEE Trans Med Robot
Bionics 2021;3:623–39. DOI
10. Chen X, Zhang K, Liu H, Leng Y, Fu C. A probability distribution model-based approach for foot placement prediction in the early swing
phase with a wearable IMU sensor. IEEE Trans Neural Syst Rehab Eng 2021;29:2595–604. DOI
11. Chen X, Liu Z, Zhu J, Zhang K, Leng Y, Fu C. Comparison of machine learning regression algorithms for foot placement prediction. In:
2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP); 2021 Nov 26-28; Shanghai, China. IEEE;
2021. pp. 169–74. DOI
12. Leng Y, Huang G, Ma L, et al. A lightweight, integrated and portable force-controlled ankle exoskeleton for daily walking assistance. In:
2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP); 2021 Nov 26-28; Shanghai, China. IEEE;
2021. pp. 42–7. DOI
13. Leng Y, Lin X, Yang L, Zhang K, Chen X, Fu C. A model for estimating the leg mechanical work required to walk with an elastically
suspended backpack. IEEE Trans Human Mach Syst 2022;52:1303–12. DOI
14. Chen C, Zhang K, Leng Y, Chen X, Fu C. Unsupervised sim-to-real adaptation for environmental recognition in assistive walking. IEEE
Trans Neural Syst Rehab Eng 2022;30:1350–60. DOI
15. Ma T, Wang Y, Chen X, et al. A piecewise monotonic smooth phase variable for speed-adaptation control of powered knee-ankle prostheses.
IEEE Robot Autom Lett 2022;7:8526–33. DOI
16. Chen X, Chen C, Wang Y, et al. A piecewise monotonic gait phase estimation model for controlling a powered transfemoral prosthesis in
various locomotion modes. IEEE Robot Autom Lett 2022;7:9549–56. DOI
17. Chen C, Cao Y, Chen X, Wu D, Xiong C, Huang J. A fused deep fuzzy neural network controller and its application to pneumatic flexible
joint. IEEE/ASME Trans Mech 2023;28:3214–25. DOI
18. Chen N, Chen X, Chen C, Leng Y, Fu C. Research on the human-following method, fall gesture recognition, and protection method for
the walking-aid cane robot. In: 2022 IEEE International Conference on Cyborg and Bionic Systems (CBS); 2023 Mar 24-26; Wuhan,
China. IEEE; 2023. pp. 286–91. DOI
19. Wakita K, Huang J, Di P, Sekiyama K, Fukuda T. Human-walking-intention-based motion control of an omnidirectional-type cane robot.
IEEE/ASME Trans Mech 2013;18:285–96. DOI
20. Di P, Hasegawa Y, Nakagawa S, et al. Fall detection and prevention control using walking-aid cane robot. IEEE/ASME Trans Mech
2016;21:625–37. DOI
21. Wang E, Chen X, Li Y, Fu Z, Huang J. Lower-limb motion intent recognition based on sensor fusion and fuzzy multi-task learning. IEEE
Trans Fuzzy Syst 2024;32:2903-14. DOI
22. Cong Y, Li X, Liu J, Tang Y. A stairway detection algorithm based on vision for UGV stair climbing. In: 2008 IEEE International
Conference on Networking, Sensing and Control; 2008 Apr 06-08; Sanya, China. IEEE; 2008. pp. 1806–11. DOI
23. Murakami S, Shimakawa M, Kivota K, Kato T. Study on stairs detection using RGB-depth images. In: 2014 Joint 7th International
Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS);
2014 Dec 03-06; Kitakyushu, Japan. IEEE; 2014. pp. 1186–91. DOI
24. Sriganesh P, Bagree N, Vundurthy B, Travers M. Fast staircase detection and estimation using 3D point clouds with multi-detection
merging for heterogeneous robots. In: 2023 IEEE International Conference on Robotics and Automation (ICRA); 2023 May 29 - Jun 02;
London, United Kingdom. IEEE; 2023. pp. 9253–59. DOI
25. Wang C, Pei Z, Qiu S, Tang Z. Deep leaning-based ultra-fast stair detection. Sci Rep 2022;12:16124. DOI
26. Zhou QY, Park J, Koltun V. Open3D: a modern library for 3D data processing. arXiv. [Preprint] Jan 30, 2018. [accessed on 2024 May 8].
Available from: http://dx.doi.org/https://doi.org/10.48550/arXiv.1801.09847.
27. Matsumura H, Premachandra C. Deep-learning-based stair detection using 3D point cloud data for preventing walking accidents of the
visually impaired. IEEE Access 2022;10:56249–55. DOI
28. Ramanathan M, Luo L, Er JK, et al. Visual environment perception for obstacle detection and crossing of lower-limb exoskeletons. In:
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2022 Oct 23-27; Kyoto, Japan. IEEE; 2022. pp.
12267–74. DOI
29. Liu DX, Xu J, Chen C, Long X, Tao D, Wu X. Vision-assisted autonomous lower-limb exoskeleton robot. IEEE Trans Syst Man Cybern
Syst 2021;51:3759–70. DOI
30. Zhang K, Xiong C, Zhang W, et al. Environmental features recognition for lower limb prostheses toward predictive walking. IEEE Trans
Neural Syst Rehab Eng 2019;27:465–76. DOI
31. Chen C, Chen X, Yin S, et al. Enhancing prosthetic safety and environmental adaptability: a visual-inertial prosthesis motion estimation
approach on uneven terrains. arXiv. [Preprint] Apr 29, 2024. [accessed on 2024 May 8]. Available from: https://doi.org/10.48550/arXiv
.2404.18612.
32. Oßwald S, Gutmann JS, Hornung A, Bennewitz M. From 3D point clouds to climbing stairs: a comparison of plane segmentation
approaches for humanoids. In: 2011 11th IEEE-RAS International Conference on Humanoid Robots; 2011 Oct 26-28; Bled, Slovenia.
IEEE; 2011. pp. 93–8. DOI
33. Pomerleau F, Liu M, Colas F, Siegwart R. Challenging data sets for point cloud registration algorithms. Int J Robot Res 2012;31:1705–
11. DOI
34. Maken FA, Ramos F, Ott L. Bayesian iterative closest point for mobile robot localization. Int J Robot Res 2022;41:851–74. DOI