Abstract
Mechanical vibrations due to uneven terrains can significantly impact the accuracy of computer vision systems installed on any moving vehicle. In this study, we investigate the impact of mechanical vibrations induced using artificial bumps in a controlled environment on the performance of smart computer vision systems installed on an Electrical powered Wheelchair (EPW). Besides, the impact of the vibrations on the user’s health and comfort is quantified using the vertical acceleration of an Inertial Measurement Unit (IMU) sensor according to the ISO standard 2631. The proposed smart computer vision system is a semantic segmentation based on deep learning for pixels classification that provides environmental cues for visually impaired users to facilitate safe and independent navigation. In addition, it provides the EPW user with the estimated distance to objects of interest. Results show that a high level of vibrations can negatively impact the performance of the computer vision system installed on powered wheelchairs. Also, high levels of whole-body vibrations negatively impact the user’s health and comfort.
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References
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994). https://doi.org/10.1109/72.279181
Bruno, D.R., Osorio, F.S.: Image classification system based on deep learning applied to the recognition of traffic signs for intelligent robotic vehicle navigation purposes. In: 2017 Latin American Robotics Symposium (LARS) and 2017 Brazilian Symposium on Robotics (SBR), pp. 1–6 (2017). https://doi.org/10.1109/SBR-LARS-R.2017.8215287
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801–818 (2018)
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)
Ess, A., Schindler, K., Leibe, B., Gool, L.V.: Object detection and tracking for autonomous navigation in dynamic environments. Int. J. Robot. Res. 29(14), 1707–1725 (2010). https://doi.org/10.1177/0278364910365417
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Teh, Y.W., Titterington, M. (eds.) Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, PMLR, Chia Laguna Resort, Sardinia, 13–15 May 2010, vol. 9, pp. 249–256 (2010). http://proceedings.mlr.press/v9/glorot10a.html
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Mechanical vibration and shock - evaluation of human exposure to whole-body vibration - part 1: general requirements. In: Standard, International Organization for Standardization, Geneva, CH, May 1997 (1997)
Jiang, H., Zhang, T., Wachs, J.P., Duerstock, B.S.: Enhanced control of a wheelchair-mounted robotic manipulator using 3-d vision and multimodal interaction. Comput. Vis. Image Understand. 149, 21–31 (2016). https://doi.org/10.1016/j.cviu.2016.03.015,https://www.sciencedirect.com/science/article/pii/S1077314216300066, special issue on Assistive Computer Vision and Robotics - "Assistive Solutions for Mobility, Communication and HMI"
Marichal, G.N., Tomás-Rodríguez, M., Hernández, Á., Castillo-Rivera, S., Campoy, P.: Vibration reduction for vision systems on board UAV using a neuro-fuzzy controller
Miyamoto, R., et al.: Vision-based road-following using results of semantic segmentation for autonomous navigation. In: 2019 IEEE 9th International Conference on Consumer Electronics (ICCE-Berlin), pp. 174–179 (2019). https://doi.org/10.1109/ICCE-Berlin47944.2019.8966198
Mohamed, E., Sirlantzis, K., Howells, G.: Indoor/outdoor semantic segmentation using deep learning for visually impaired wheelchair users. IEEE Access 9, 147914–147932 (2021). https://doi.org/10.1109/ACCESS.2021.3123952
Mohamed, E., Sirlantzis, K., Howells, G., Dib, J.: Investigation of vibration and user comfort for powered wheelchairs. IEEE Sens. Lett. 6(2), 1–4 (2022). https://doi.org/10.1109/LSENS.2022.3147740
Periu, C., Mohsenimanesh, A., Laguë, C., McLaughlin, N.: Isolation of vibrations transmitted to a lidar sensor mounted on an agricultural vehicle to improve obstacle detection. Canadian Biosyst. Eng. 55 (2013)
Quintero, C.P., Ramirez, O., Jägersand, M.: Vibi: assistive vision-based interface for robot manipulation. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 4458–4463 (2015). https://doi.org/10.1109/ICRA.2015.7139816
Rashed, H., El Sallab, A., Yogamani, S., ElHelw, M.: Motion and depth augmented semantic segmentation for autonomous navigation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2019)
Acknowledgment
This work is supported by the Assistive Devices for empowering dis-Abled People through robotic Technologies (ADAPT) project. ADAPT is selected for funding by the INTERREG VA France (Channel) England Programme which is co-financed by the European Regional Development Fund (ERDF). The European Regional Development Fund (ERDF) is one of the main financial instruments of the European Unions (EU) cohesion policy.
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Mohamed, E., Sirlantzis, K., Howells, G. (2022). Analysing the Impact of Vibrations on Smart Wheelchair Systems and Users. In: El Yacoubi, M., Granger, E., Yuen, P.C., Pal, U., Vincent, N. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2022. Lecture Notes in Computer Science, vol 13363. Springer, Cham. https://doi.org/10.1007/978-3-031-09037-0_3
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