Abstract

The aim is to identify stable and critical recognition in children and pregnant women by wearable interface based monitoring system. The objective of this research proposal is to identify safe and danger zone prediction in toddler and normal and contra pain in pregnant women. The technology supporting this analysis of gesture has advanced dramatically. Past decades of remote health care monitoring have provided us with significant knowledge about the accuracy of tests performed. Mainly motivated by increasing healthcare costs and propelled by recent technological advances in miniature bio sensing devices, smart textiles, microelectronics and wireless communications, the continuous advance of wearable sensor-based systems will potentially transform the future of healthcare by enabling proactive personal health management and ubiquitous monitoring of a toddler and pregnant women health condition.The remote healthcare monitoring on a care taking base involves many implicit observations between the subjects and the care takers. Any ignorance and negligence leads to unpleasant situations thereafter. A simple wearable attire system can precisely interpret the implicit communication of the state of the subject and pass it to the care takers or to an automated aid device. Casual and conventional movements of subjects during play and living condition can be used for the above purpose. The methodology suggests a novel way of identifying safe and unsafe conditions of playing for the children as well as normal and critical situations of pregnant women where a medical assistance is desperately required. The experimental results show a well-distinguished realization of different body movement activities using a wearable attire array medium and the interpretation results always show significant and identifiable thresholds.

Keywords:

Affective-gesture computing, bio-signal processing, remote monitoring, wearable computing,

References

1. Baek, H.J., G.S. Chung, K.K. Kim and K.S. Park, 2012. A smart health monitoring chair for nonintrusive measurement of biological signals. IEEE T. Inf. Technol. B., 16(1): 150-158.
   CrossRef    PMid:22086543    
2. Beckmann, L., C. Neuhaus, G. Medrano, N. Jungbecker, M. Walter, T. Gries and S. Leonhardt, 2010. Characterization of textile electrodes and con-ductors using standardized measurement setups. Physiol. Meas., 31(2): 233-247.
   CrossRef    PMid:20086274    
3. Blanz, V., B. Scholkopf, H. Bulthoff, C. Burges, V. Vapnik and T. Veter, 1996. Comparison of view-based object recognition algorithms using realistic 3D models. Lect. Notes Comput. Sci., 1112: 251-256.
   CrossRef    
4. Bruce, L.M., C.H. Koger and L. Jiang, 2002. Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction. IEEE T. Geosci. Remote, 40(10): 2331-2338.
   CrossRef    
5. Chi, Y.M., T.P. Jung and G. Cauwenerghs, 2010. Dry-contact and noncontact biopotential electrodes: Methodological review. IEEE Rev. Biomed. Eng., 3: 106-119.
   CrossRef    PMid:22275204    
6. Cortes, C. and V. Vapnik, 1995. Support-vector networks. Mach. Learn., 20(3): 273-297.
   CrossRef    
7. Eilebrecht, B., A. Schommartz, M. Walter, T. Wartzek and M. Czaplik, 2010. A capacitive ECG array with visual patient feedback. Proceeding of annual International Conference of the IEEE Engineering Medicine and Biology Society (EMBC, 2010), pp: 6539-6542.
   CrossRef    
8. Guraliuc, A.R., P. Barsocchi, F. Potort`i and P. Nepa, 2011. Limb movements classification using wearable wireless transceivrs. IEEE T. Inf. Technol. B., 15(3): 474-480.
   CrossRef    PMid:21349794    
9. Ibarguren, A., I. Maurtua and B. Sierra, 2010. Layered architecture for real time sign recognition: Hand gesture and movement. Eng. Appl. Artif. Intel., 23(7): 1216-1228.
   CrossRef    
10. Kamel, N.S., S. Sayeed and G.A. Ellis, 2008. Glove-based approach to online signature verification. IEEE T. Pattern Anal., 30(6): 1109-1113.
    CrossRef    PMid:18421114    
11. Lin, B.S., W. Chou, H.Y. Wang, Y.J. Huang and J.S. Pan, 2009. Development of novel non-contact electrodes for mobile electrocardiogram monitoring system. IEEE J. Translat. Eng. Health Med., Vol. 1, ISSN: 2168-2327.
    
12. Lin, C.T., L.D. Liao, Y.H. Liu, I.J. Wang, B.S. Lin and J.Y. Chang, 2011. Novel dry polymer foam electrodes for long-term EEG measurement. IEEE T. Biomed. Eng., 58(5): 1200-1207.
    CrossRef    PMid:21193371    
13. Lee, J. and M. Verleysen, 2007. Nonlinear Dimensionality Reduction. Springer Verlag.
    CrossRef    
14. Matsuda, T. and M. Makikawa, 2008. ECG monitoring of a car driver using capacitively-coupled electrodes. Proceeding of 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS, 2008), pp: 1315-1318.
    CrossRef    
15. Ng, H., W.H. Tan, H.L. Tong and J. Abdullah, 2009. Extraction of human gait features from enhanced human silhouette images. Proceeding of the IEEE International Conference on Signal and Image Processing Applications (ICSIPA, 2009), pp: 425-430.
    
16. Oehler, M., V. Ling, K. Melhorn and M. Schilling, 2008. A multichannel portable ECG system with capacitive sensors. Physiol. Meas., 29(7): 783-793.
    CrossRef    PMid:18560053    
17. Scholkopf, B., C. Burges and V. Vapnik, 1995. Extracting support data for a given task. Proceeding of the 1st International Conference on Knowledge Discovery and Data Mining. AAAI Press, Menlo Park, CA.
    
18. Scholkopf, B., C. Burges and V. Vapnik, 1996. Incorporating invariances in support vector learning machines. In: von der Malsburg, C., W. von Seelen, J.C. Vorbr ́luggen and B. Sendhoff (Eds.), Artificial Neural Networks ICANN'96. Lecture Notes in Computer Science, Springer, Berlin, 1112: 47-52.
    CrossRef    
19. Sergios, T. and K. Autrombas, 2009. Pattern Recognition. Elsevier Inc., ISBN: 978-1-59749-272-0.
    
20. Sheryl, O.A., A. Amraj and N. Maheswari, 2013. Fuzzy ARTMAP based classification feature for danger and safety zone prediction for toddlers using wearable electrodes. Proceeding of IEEE International Conference on High Performance Green Computing.
    
21. Stacy, J.M. and J.A. Paradiso, 2002. A compact wearable sensor package for clinical gait monitoring. OFFSPRING, 1(1).
    
22. Stephen, M., 2009. Machine Learning: An Algorithmic Perspective. CRC Press a Chapman and Hall Book, Taylor and Francis Group.
    
23. Xu, G. and Y. Lu, 2006. Channel and modulation selection based on support vector machines for cognitive radio. Proceeding of International Conference on Wireless Communications, Networking and Mobile Computing (WICOM, 2006), pp: 1-4.
    CrossRef    
24. Zhang, F., X. Jiaotong, Z. Geng and W. Yuan, 2009. The algorithm of interpolating windowed FFT for harmonic analysis of electric power system. IEEE T. Power Deliver., 16(2): 160-164.
    CrossRef    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.