240
Views
0
CrossRef citations to date
0
Altmetric
Original Articles

Head Movement Based Interaction in Mobility

ORCID Icon &

References

  • Al-Rahayfeh A., Faezipour M. (2013). Eye tracking and head movement detection: a state-of-art survey. IEEE Journal of Translational Engineering in Health and Medicine, 1, doi: http://dx.doi.org/10.1109/JTEHM.2013.2289879.
  • Berjón R., Mateos M., Barriuso A.L., Muriel I., Villarrubia G. (2011). Alternative human-machine interface system for powered wheelchairs. Proc. IEEE 1st International Conference on Serious Games and Applications for Health (SeGAH), 1–5. doi: http://doi.ieeecomputersociety.org/10.1109/SeGAH.2011.6165452.
  • Calvo A.A., Perugini S. (2014). Pointing devices for wearable computers. Advances in human-computer interaction, Article ID 527320, 10 pages.doi: http://dx.doi.org/10.1155/2014/527320.
  • Cristianini N., Shawe-Taylor J. (2000). An introduction to support vector machines and other kernel-based methods. Cambridge University Press.
  • Cyganek B., Siebert J.P. (2009). An Introduction to 3D Computer Vision Techniques and Algorithms. John Wiley & Sons, Inc.
  • de la Barré R., Chojecki P., Leiner U., Mühlbach L., Ruschin D. (2009). Touchless Interaction-Novel Chances and Challenges. Proc. of Human-Computer Interaction. Novel Interaction Methods and Techniques. HCI 2009. In: Lecture Notes in Computer Science 5611, 161–169, Springer. doi: http://dx.doi.org/10.1007/978-3-642-02577-8_18
  • Ergonomics of human-system interaction – Part 411: Evaluation methods for the design of physical input devices. ISO Standard: ISO 9241-411:2012.
  • Evans D.G., Drew R., Blenkhorn P. (2000). Controlling Mouse Pointer Position Using an Infrared Head-Operated Joystick. IEEE Transactions on Rehabilitation Engineering, 8(1), 107–117.
  • Hashem S.Y., Zin N.A., Yatim N.F., Ibrahim N.M. (2014). Improving mouse controlling and movement for people with Parkinson’s disease and involuntary tremor using adaptive path smoothing technique via B-spline. Assistive Technology, 26(2), 96–104. doi: http://dx.doi.org/10.1080/10400435.2013.845271.
  • Iwata M., Ebisawa Y. (2008). PupilMouse supported by head pose detection. Proc. IEEE International Conference on Virtual Environments, Human-Computer Interfaces, and Measurement Systems, 178–183. doi: http://dx.doi.org/10.1109/VECIMS.2008.4592776.
  • Jaimes A., Sebe N. (2007). Multimodal human computer interaction: a survey. Computer Vision and Image Understanding, 108(1-2), (Special Issue on Vision for Human-Computer Interaction), 116–134.
  • Jalaliniya S., Mardanbegi D., Pederson T. (2015). MAGIC pointing for eyewear computers. Proceedings of the ISWC’15, Osaka, Japan, doi: http://dx.doi.org/10.1145/2802083.2802094.
  • Jeong-Hoon Shin and Kwang-Seok Hong (2005). An improved HCI method and information input device using gloves for wearable computers. Advances in Informatics, Lecture Notes in Computer Science, 3746, 286–295.
  • Jian-Zheng L., Zheng Z. (2011). Head movement recognition based on LK algorithm and Gentleboost. Proc. 7th International Conference on Networked Computing and Advanced Information Management (NCM), 232–236.
  • Kapoor A., Picard R.W. (2001). A real-time head nod and shake detector. Proc. 2001 Workshop on perceptive user interfaces, 1–5, doi: http://dx.doi.org/10.1145/971478.971509.
  • Kim J., et al. (2008). Construction of integrated simulator for developing head/eye tracking system. Proc. International Conference on Control, Automation and Systems (ICCAS), 2485–2488, doi: http://dx.doi.org/10.1109/ICCAS.2008.4694272.
  • Kim S, Park M., Anumas S., Yoo J. (2010). Head mouse system based on gyro- and opto-sensors. Proc. 3rd International Conference on Biomedical Engineering and Informatics (BMEI), 4, 1503–1506, doi: http://dx.doi.org/10.1109/BMEI.2010.5639399.
  • Kim H., Ryu D. (2006). Computer control by tracking head movements for the disabled. Proceedings of the ICCHP ’06. In: Lecture Notes in Computer Science, 4061, 709–715, Springer.
  • King L.M., Nguyen H.T., Taylor P.B. (2005). Hands-free head-movement gesture recognition using artificial neural networks and the magnified gradient function. Proc. 27th Annual International Conference of the Engineering in Medicine and Biology Society, (IEEE-EMBS), 2063–2066, doi: http://dx.doi.org/10.1109/IEMBS.2005.1616864.
  • Kowalczyk P., Sawicki D. (2017). “Computer controlling device,” (in Polish: Urządzenie do sterowania komputera), Patent Number PL402798, granted on 7 June 2017, by Polish Patent Office.
  • Kupetz D.J., Wentzell S.A., BuSha B.F. Head motion controlled power wheelchair. Proc. IEEE 36th Annual. Northeast Bioengineering Conference, 1–2. doi: http://dx.doi.org/10.1109/NEBC.2010.5458224.
  • Liu K., Luo Y.P., Tei G., Yang S.Y. (2008). Attention recognition of drivers based on head pose estimation. Proc. IEEE Vehicle Power and Propulsion Conference, 1–5, doi: http://dx.doi.org/10.1109/VPPC.2008.4677536.
  • Lowe D.G. (1999). Object recognition from local scale-invariant features. Proc. of the Seventh IEEE International Conference on Computer Vision, doi: http://dx.doi.org/10.1109/ICCV.1999.790410.
  • MacKenzie I.S., Tanaka-Ishii K. (2007). Text Entry Systems: Mobility, Accessibility, Universality. Burlington, MA, USA: Morgan Kaufmann.
  • Majaranta P., Aoki H., Donegan M., Hansen D.W., Hansen J.P., Hyrskykari A., Räihä K-J. (2011). Gaze Interaction and Applications of Eye Tracking: Advances in Assistive Technologies. IGI Global.
  • Mandal B., Eng H-L., Lu H., Chan D.W.S., Ng Y-L. (2012). Non-intrusive head movement analysis of videotaped seizures of epileptic origin. Proc. 34th Annual International Conference of the IEEE EMBS, 6060–6063.
  • Manogna S., Vaishnavi S., Geethanjali B. (2010). Head movement based assist system for physically challenged. Proc. 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE), 1–4, doi: http://dx.doi.org/10.1109/ICBBE.2010.5517790.
  • Moon I, Lee M., Chu J., Mun M. (2005). Wearable EMG-based HCI for electric-powered wheelchair users with motor disabilities. Proc. of the 2005 IEEE International Conference on Robotics and Automation, 2649–2654, doi: http://dx.doi.org/10.1109/ROBOT.2005.1570513.
  • Moreno F., Tarrida A., Andrade-Cetto J., Sanfeliu A. (2002). 3D real-time head tracking fusing color histograms and stereovision. Proc. 16th International Conference on Pattern Recognition (ICPR), 1, 368–371.
  • Murphy-Chutorian E., Trivedi M.M. (2010). Head pose estimation and augmented reality tracking: An integrated system and evaluation for monitoring driver awareness, IEEE Trans. on Intelligent Transportation Systems, 11(2), 300–311, doi: http://dx.doi.org/10.1109/TITS.2010.2044241.
  • Nguyen S.T., Nguyen H.T., Taylor P.B., Middleton J. (2006). Improved head direction command classification using an optimized Bayesian neural network. Proc. 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS ‘06), 5679–5682, doi: http://dx.doi.org/10.1109/IEMBS.2006.260430.
  • Perez E., et al. (2013). Robust human machine interface based on head movements applied to assistive robotics. The Scientific World Journal, 2013, Article ID 589636, doi: http://dx.doi.org/10.1155/2013/589636.
  • Rabiner L. (1989) A tutorial on Hidden Markov Models and selected applications in speech recognition. Proceedings of IEEE, 77, 2, February 1989, 257–286. doi: http://dx.doi.org/10.1109/5.18626.
  • Rougier C., Meunier J., St-Arnaud A., Rousseau J. (2013). 3D head tracking for fall detection using a single calibrated camera. Image and Vision Computing, 31, 246–254.
  • Sasou A. (2009). Acoustic head orientation estimation applied to powered wheelchair control. Proc. 2nd International Conference on Robot Communication and Coordination (ROBOCOMM ‘09), 1–6.
  • Satoh K., Uchiyama S., Yamamoto H. (2004). A head tracking method using bird’s-eye view camera and gyroscope. Proc. 3rd IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR), 202–211, doi: http://dx.doi.org/10.1109/ISMAR.2004.3.
  • Schwaller A. (2014). “Combining eye- and head-tracking signals for improved event detection,” Master’s Thesis. Lund University.
  • Singh H., Singh J. (2012). Human eye tracking and related issues: a review,” International Journal of Scientific and Research Publications, 2(9), 1–9.
  • Siriteerakul T., Sato Y., Boonjing V. (2011). Estimating change in head pose from low resolution video using LBP-based tracking. Proc. International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS), 1–6. doi: http://dx.doi.org/10.1109/ISPACS.2011.6146173.
  • Song Y., Luo Y., Lin J. (2011). Detection of movements of head and mouth to provide computer access for disabled. Proc. International Conference on Technologies and Applications of Artificial Intelligence (TAAI), 223–226, doi: http://dx.doi.org/10.1109/TAAI.2011.46.
  • Soukoreff R.W., MacKenzie I.S. (2003). Metrics for text entry research: An evaluation of MSD and KSPC, and a new unified error metric. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI), 113–120.
  • Strumiłło P., Pajor T. (2012). A vision-based head movement tracking system for human-computer interfacing. Proc. New trends in audio and video/signal processing algorithms, architectures, arrangements and applications (NTAV/SPA), 143–147.
  • Tomasi C., Kanade T. (1991). Detection and tracking of point features. Technical Report. Carnegie Mellon University, CMU CS-91-132.
  • Viola P., Jones M. (2001). Rapid Object Detection using a Boosted Cascade of Simple Features. Computer Vision and Pattern Recognition (CVPR,. 1, 511–518, doi: http://dx.doi.org/10.1109/CVPR.2001.990517.
  • Witt H. (2007). “Human-computer interfaces for wearable computers, a systematic approach to development and evaluation.” PhD Thesis. University Bremen.
  • Zarchan P., Musoff H. (2000). Fundamentals of Kalman Filtering: A Practical Approach. (sec. ed.) American Institute of Aeronautics and Astronautics, Incorporated.
  • Zeltzer D., Sturman D. (1994). A survey of glove-based input. IEEE Computer Graphics and Applications, 14(1), 30–39, doi: http://dx.doi.org/10.1109/38.250916.
  • Zhao Z., Wang Y., Fu S. (2012). Head movement recognition based on Lucas-Kanade algorithm. Proc. International Conference on Computer Science & Service System (CSSS), 2303–2306, doi: http://dx.doi.org/10.1109/CSSS.2012.571.
  • Zhao Y, Yan H. (2011). Head orientation estimation using neural network. Proc. International Conference on Computer Science and Network Technology (ICCSNT), 3, 2075–2078, doi: http://dx.doi.org/10.1109/ICCSNT.2011.6182379.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.