Emergency Gesture Communication by Patients, Elderly and Differently Abled with Care Takers Using Wearable Data Gloves

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DOI: 10.4236/jsip.2013.41001    3,730 Downloads   6,157 Views   Citations

ABSTRACT

The Healthcare monitoring on a clinical base involves many implicit communication between the patient and the care takers. Any misinterpretation leads to adverse effects. A simple wearable system can precisely interpret the implicit communication to the care takers or to an automated support device. Simple and obvious hand movements can be used for the above purpose. The proposed system suggests a novel methodology simpler than the existing sign language interpretations for such implicit communication. The experimental results show a well-distinguished realization of different hand movement activities using a wearable sensor medium and the interpretation results always show significant thresholds.

Cite this paper

K. Rajendran, A. Samraj and M. Rajavel, "Emergency Gesture Communication by Patients, Elderly and Differently Abled with Care Takers Using Wearable Data Gloves," Journal of Signal and Information Processing, Vol. 4 No. 1, 2013, pp. 1-9. doi: 10.4236/jsip.2013.41001.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] R. Gentner and J. Classen “Development and Evaluation of a Low-Cost Sensor Glove for Assessment of Human Finger Movements in Neurophysiologic Settings,” Journal of Neuroscience Methods, Vol. 178, No. 1, 2009, pp. 138-147. doi:10.1016/j.jneumeth.2008.11.005
[2] www.5dt.com
[3] A. Ibarguren, I. Maurtua and B. Sierra, “Layered Architecture for Real Time Sign Recognition: Hand Gesture and Movement,” Engineering Applications of Artificial Intelligence, Vol. 23, No. 7, 2010, pp. 1216-1228. doi:10.1016/j.engappai.2010.06.001
[4] C. Oz and M. C. Leu “American Sign Language Word Recognition with a Sensory Glove Using Artificial Neural Networks,” Engineering Applications of Artificial Intelligence, Vol. 24, No. 7, 2011, pp. 1204-1213. doi:10.1016/j.engappai.2011.06.015
[5] S. Sayeed, R. Besar and N. S. Kamel “Dynamic Signature Verification Using Sensor Based Data Glove,” ICSP 2006 Proceedings, Beijing, 16-20 November 2006, pp. 2387-2390.
[6] S. Sayeed, N. S. Kamel and R. Besar, “A Novel Approach to Dynamic Signature Verification Using Sensor-Based Data Glove,” American Journal of Applied Sciences, Vol. 6, No. 2, 2009, pp. 233-240.
[7] N. S. Kamel, S. Sayeed and G. A. Ellis, “Glove-Based Approach to Online Signature Verification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 6, 2008, pp. 1109-1113. doi:10.1109/TPAMI.2008.32
[8] C. K. Loo, A. Samraj and G. C. Lee, “Evaluation of Methods for Estimating Fractal Dimension in Motor Imagery Based Brain Computer Interface,” Discrete Dynamics in Nature and Society, Vol. 2011, 2011, Article ID: 724697.
[9] U. Gu?lu, Y. Gucluturk and C. K. Loo “Evaluation of Fractal Dimension Estimation Methods for Feature Extraction in Motor Imagery Based Brain Computer Interface,” Procedia Computer Science, Vol. 3, 2011, pp. 589-594. doi:10.1016/j.procs.2010.12.098
[10] S. Sayeed, S. Andrews, R. Besar and L. C. Kiong “Forgery Detection in Dynamic Signature Verification by Entailing Principal Component Analysis”, Discrete Dynamics in Nature and Society, Vol. 2007, 2007, Article ID: 70756. doi:10.1155/2007/70756

  
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