Advertisement

ManDri: A New Proposal of Manus VR Facility Integration in Everyday Car Driving

  • Walter BalzanoEmail author
  • Maurizio Minieri
  • Silvia Stranieri
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)

Abstract

The purpose of this paper is to analyze all the possible uses of the Manus VR in everyday driving, to make it easier and safer. With these technological gloves, equipped with eleven sensors for each hand, able to precisely pick up every movement of the hand and fingers, it is possible to associate various gestures to specific actions. In particular, they allow to turn, accelerate, brake, activate the car turn signals, the air conditioning, even alarm, everything just using simple gestures. But ManDri is not only framed in today’s guide: indeed, a glove, being equipped with two IMUs (inertial measurement units), can be integrated with GPS (global position system). In fact, progresses in these years allow the production of IMU-enabled GPS devices, that allow a GPS receiver to work even when GPS-signals are unavailable, such as inside buildings, tunnels, or places characterized by electronic interference. Moreover, it is also possible to create the ManDri app for all the devices so that, after connecting them to the gloves, dedicated accounts can be created for chosen people allowed to use them, making easier the control, the management, and the gloves gestures customization. This device unavoidably becomes essential and difficult to replace after trying it. These positive premises make us understand how advantageous the choice to use these gloves in everyday driving to improve it, make it safer, and undoubtedly facilitate it.

Keywords

Manus VR ManDri IMU GPS 

References

  1. 1.
    Ahmed, A., Loo, C., Obo, T.: Neuro-fuzzy model with subtractive clustering optimization for arm gesture recognition by angular representation of kinect data. In: 6th ICIEV-ISCMHT, pp. 1–6. IEEE (2017)Google Scholar
  2. 2.
    Balzano, W., Murano, A., Stranieri, S.: Logic-based clustering approach for management and improvement of VANETs. J. High Speed Netw. 23(3), 225–236 (2017)CrossRefGoogle Scholar
  3. 3.
    Balzano, W., Murano, A., Vitale, F.: Hypaco–a new model for hybrid paths compression of geodetic tracks. Int. J. Grid Util. Comput. (2017)Google Scholar
  4. 4.
    Balzano, W., Murano, A., Vitale, F.: SNOT-WiFi: sensor network-optimized training for wireless fingerprinting. J. High Speed Netw. 24(1), 79–87 (2018)CrossRefGoogle Scholar
  5. 5.
    Balzano, W., Del Sorbo, M.R., Murano, A., Stranieri, S.: A logic-based clustering approach for cooperative traffic control systems. In: 3PGCIC. Springer (2016)Google Scholar
  6. 6.
    Balzano, W., Stranieri, S.: Cooperative localization logic schema in vehicular ad hoc networks. In: International Conference on NBis, pp. 960–969. Springer (2018)Google Scholar
  7. 7.
    Balzano, W., Stranieri, S.: A logic range-free algorithm for localization in wireless sensor networks. In: The 24th International DMSVIVA (2018)Google Scholar
  8. 8.
    Balzano, W., Vitale, F.: RADS: a smart road anomalies detection system using vehicle-2-vehicle network and cluster featuresGoogle Scholar
  9. 9.
    Balzano, W., Vitale, F.: FiDGP: a smart fingerprinting radiomap refinement method based on distance-geometry problem. In: International Conference on NBis, pp. 970–978. Springer (2018)Google Scholar
  10. 10.
    Balzano, W., Vitalem F.: GER-EN–GNSS error reduction using an elastic network based on V2V and LIDAR. In: International Symposium on Cyberspace Safety and Security, pp. 124–131. Springer (2018)Google Scholar
  11. 11.
    Benmoussa, M., Mahmoudi, A.: Machine learning for hand gesture recognition using bag-of-words. In: ISCV, pp. 1–7. IEEE (2018)Google Scholar
  12. 12.
    Bhuiyan, R., Tushar, A., Ashiquzzaman, A., Shin, J., Islam, M.: Reduction of gesture feature dimension for improving the hand gesture recognition performance of numerical sign language. In: 20th International Conference of ICCIT, pp. 1–6. IEEE (2017)Google Scholar
  13. 13.
    Chiang, T., Fan, C.: 3D depth information based 2D low-complexity hand posture and gesture recognition design for human computer interactions. In: 3rd ICCCS, pp. 233–238. IEEE (2018)Google Scholar
  14. 14.
    Dai, G., Wang, P.: Design of intelligent car based on WiFi video capture and OpenCV gesture control. In: CAC, pp. 4103–4107. IEEE (2017)Google Scholar
  15. 15.
    Dai, G., Yu, L., Huang, J.: Dynamic and interactive gesture recognition algorithm based on kinect. In: CCDC, pp. 3479–3484. IEEE (2017)Google Scholar
  16. 16.
    Dehankar, A., Jain, S., Thakare, V.: Performance analysis of RTEPI method for real time hand gesture recognition. In: ICISS, pp. 1031–1036. IEEE (2017)Google Scholar
  17. 17.
    Gajjar, V., Mavani, V., Gurnani, A.: Hand gesture real time paint tool-box: machine learning approach. In: ICPCSI, pp. 856–860. IEEE (2017)Google Scholar
  18. 18.
    Hussain, S., Saxena, R., Han, X., Khan, J., Shin, H.: Hand gesture recognition using deep learning. In: ISOCC, pp. 48–49. IEEE (2017)Google Scholar
  19. 19.
    Khan, M., Mishra, K., Qadeer, M.: Gesture recognition using OpenCV. In: 7th CSNT, pp. 167–171. IEEE (2017)Google Scholar
  20. 20.
    Lei, T., Jia, X., Zhang, Y., Zhang, Y., Su, X., Liu, S.: Holoscopic 3D micro-gesture recognition based on fast preprocessing and deep learning techniques. In: Automatic Face & Gesture Recognition (FG 2018), pp. 795–801. IEEE (2018)Google Scholar
  21. 21.
    Magoulès, F., Zou, Q.: GPU accelerated contactless human machine interface for driving car. In: 16th International Symposium on DCABES, pp. 7–10. IEEE (2017)Google Scholar
  22. 22.
    Maharani, D., Fakhrurroja, H., Machbub, C., et al.: Hand gesture recognition using k-means clustering and support vector machine. In: ISCAIE 2018, pp. 1–6. IEEE (2018)Google Scholar
  23. 23.
    Neßelrath, R., Moniri, M., Feld, M.: Combining speech, gaze, and micro-gestures for the multimodal control of in-car functions. In: 12th International Conference on IE, pp. 190–193. IEEE (2016)Google Scholar
  24. 24.
    Plouffe, G., Cretu, A.: Static and dynamic hand gesture recognition in depth data using dynamic time warping. IEEE Trans. Instrum. Meas. 65(2), 305–316 (2016)CrossRefGoogle Scholar
  25. 25.
    Rau, M., Agarkar, B.: 3D GRS for car infotainment. In: International Conference on ICICET, pp. 1–4. IEEE (2018)Google Scholar
  26. 26.
    Saha, S., Lahiri, R., Konar, A., Ralescu, A., Nagar, A.: Implementation of gesture driven virtual reality for car racing game using back propagation neural network. In: SSCI, pp. 1–8. IEEE (2017)Google Scholar
  27. 27.
    Sasaki, A., Hashimoto, H.: High degree-of-freedom hand model driven by lower degree-of-freedom input. In: SII, pp. 722–727. IEEE (2017)Google Scholar
  28. 28.
    Smith, K., Csech, C., Murdoch, D., Shaker, G.: Gesture recognition using mm-wave sensor for human-car interface. IEEE Sens. Lett. 2(2), 1–4 (2018)CrossRefGoogle Scholar
  29. 29.
    Varshini, M., Vidhyapathi, C.: Dynamic fingure gesture recognition using kinect. In: ICACCCT, pp. 212–216. IEEE (2016)Google Scholar
  30. 30.
    Wang, K., Zhao, R., Ji, Q.: Human computer interaction with head pose, eye gaze and body gestures. In: Automatic Face & Gesture Recognition (FG 2018), pp. 789–789. IEEE (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Walter Balzano
    • 1
    Email author
  • Maurizio Minieri
    • 1
  • Silvia Stranieri
    • 1
  1. 1.Naples University, Federico IINaplesItaly

Personalised recommendations