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Smart Textile Suit

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Seamless Healthcare Monitoring

Abstract

Textile sensors have attempted to measure heart rate, respiratory rate, as well as moving performance. The characteristics of conductivity and elasticity of textile are used to measure angle and performance. In this chapter, the principles of motion analysis using conventional optical method, inertial sensors, and textile sensors are briefly presented. In particular, overview of textile sensors in terms of conductivity and elasticity is explained. Finally, the current topics of sensor application are introduced.

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References

  1. Miller, D. C., Boyce, B. L., Dugger, M. T., Buchheit, T. E., & Gall, K. (2007). Characteristics of a commercially available silicon-oninsulator MEMS material. Sensors and Actuators a: Physical, 138(1), 130–144.

    Google Scholar 

  2. Woodman, O. J. (2007, August). An introduction to inertial navigation (Technical report UCAM-CL-TR-696, ISSN 1476–2986). University of Cambridge, Computer Laboratory.

    Google Scholar 

  3. Sabatini, A. M., Martelloni, C., Scapellato, S., & Cavallo, F. (2005). Assessment of walking features from foot inertial sensing. IEEE Transactions on Biomedical Engineering, 52(3), 486–494. https://doi.org/10.1109/TBME.2004.840727.

    Article  Google Scholar 

  4. O’Donovan, K. J., Kamnik, R., O’Keeffe, D. T., & Lyons, G. M. (2007). An inertial and magnetic sensor based technique for joint angle measurement. Journal of Biomechanics, 40(12), 2604–2611. Epub 7 Mar 2007. https://doi.org/10.1016/j.jbiomech.2006.12.010.

    Article  Google Scholar 

  5. Rouhani, H., Favre, J., Crevoisier, X., & Aminian, K. (2012). Measurement of multi-segment foot joint angles during gait using a wearable system. Journal of Biomechanical Engineering, 134(6). https://doi.org/10.1115/1.4006674.

  6. Sabatini, A. M. (2006). Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing. IEEE Transactions on Biomedical Engineering, 53(7), 1346–1356.

    Google Scholar 

  7. Cutti, A. G., Giovanardi, A., Rocchi, L., Davalli, A., & Sacchetti, R. (2008). Ambulatory measurement of shoulder and elbow kinematics through inertial and magnetic sensors. Medical & Biological Engineering & Computing, 46(2), 169–178.

    Article  Google Scholar 

  8. El-Gohary, M., & McNames, J. (2012). Shoulder and elbow joint angle tracking with inertial sensors. IEEE Transactions on Biomedical Engineering, 59(9), 2635–2641.

    Article  Google Scholar 

  9. Schepers, H. M., Roetenberg, D., & Veltink, P. H. (2010). Ambulatory human motion tracking by fusion of inertial and magnetic sensing with adaptive actuation. Medical & Biological Engineering & Computing, 48(1), 27–37.

    Article  Google Scholar 

  10. 6510-Analog Device, available online October 2017. http://www.analog.com.

  11. STMicroelectronics. (2013). TA0343, Technical article: Everything about STMicroelectronics’ 3-axis digital MEMS gyroscopes. https://www.elecrow.com/download/TA0343.pdf.

  12. Foxlin, E., Harrington, M., & Pfeifer, G. (1998). Constellation: A wide-range wireless motion-tracking system for augmented reality and virtual set applications. Proceedings of SIGGRAPH 98, Computer Graphics Proceedings, Annual Conference Series, pp. 371–378.

    Google Scholar 

  13. Ward, J. A., Lukowicz, P., & Troster, G. (2005). Gesture spotting using wrist worn microphone and 3-axis accelerometer. In Joint Conference on Smart Objects and Ambient Intelligence, pp. 99–104.

    Google Scholar 

  14. Vlasic, D., Adelsberger, R., Vannucci, G., Barnwell, J., Gross, M., Matusik, W., & Popovic, J. (2007). Practical motion capture in everyday surroundings. ACM Transactions on Graphics, 26(3). https://doi.org/10.1145/1275808.1276421.

  15. Scilingo, E. P., Gemignani, A., Paradiso, R., Taccini, N., Ghelarducci, B., & De Rossi, D. (2005). Performance evaluation of sensing fabric for monitoring physiological and biomechanical variables. IEEE Transactions on Information in Technology Biomedicine, 9(3), 345–352.

    Article  Google Scholar 

  16. Paradiso, R., Loriga, G., Taccini, N., Gemignani, A., & Ghelarducci, B. (2005). WEALTHY – A wearable health-care system: New Frontier on e-textile. Journal of Telecommunications and Information Technology, 4, 105–113.

    Google Scholar 

  17. Gibbs, P. T., & Asada, H. H. (2005). Wearable conductive fiber sensors for multi-axis human joint angle measurements. Journal of Neuroengineering and Rehabilitation, 2, 7.

    Article  Google Scholar 

  18. Tognetti, A., Lorussi, F., Bartalesi, R., Quaglini, S., Tesconi, M., Zupone, G., & De Rossi, D. (2005). Wearable kinesthetic system for capturing and classifying upper limb gesture in post-stroke rehabilitation. Journal of Neuro-Engineering and Rehabilitation, 2, 8. https://doi.org/10.1186/1743-0003-2-8.

  19. Pacelli, M., Caldani, L., & Paradiso, R. (2013). Performances evaluation of piezoresistive fabric sensors as function of yarn structure. In 2013 35th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp. 6502–6505.

    Google Scholar 

  20. Pacelli, M., Caldani, L., & Paradiso, R. (2006). Textile piezoresistive sensors for biomechanical variables monitoring. In Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th annual international conference of the IEEE, IEEE, pp. 5358–5361.

    Google Scholar 

  21. Lorussi, F., Galatolo, S., & De Rossi, D. (2009). Textile-based electrogoniometers for wearable posture and gesture capture systems. IEEE Sensor Journal, 9(9), 1014–1024.

    Article  Google Scholar 

  22. Mattmann, C., Clemens, F., & Tröster, G. (2008). Sensor for measuring strain in textile. Sensors, 8(6), 3719–3732.

    Article  Google Scholar 

  23. Tognetti, A., Lorussi, F., Carbonaro, N., De Rossi, D., De Toma, G., & Mancuso, C. et al. (2014). Daily-life monitoring of stroke survivors motor performance: The interaction sensing system. In Engineering in Medicine and Biology Society (EMBC), 2014 36th annual international conference of the IEEE, Orlando, IEEE, 4099–4102.

    Google Scholar 

  24. Tognetti, A., Lorussi, F., Dalle Mura, G., Carbonaro, N., Pacelli, M., Paradiso, R., & De Rossi, D. (2014). New generation of wearable goniometers for motion capture systems. Journal of Neuro-Engineering and Rehabilitation, 11, 56. http://www.jneuroengrehab.com/content/11/16

  25. Roetenberg, D., Schipper, L., Garofalo, P., Cutti, A., & Luinge, H. (2010, July 14–16). Joint angles and segment length estimation using inertial sensors, 3DMA-10, San Francisco.

    Google Scholar 

  26. Lorussi, F., Scilingo, E. P., Tesconi, M., Tognetti, A., & De Rossi, D. (2005). Strain sensing fabric for hand posture and gesture monitoring. IEEE Transactions on Information Technology in Biomedicine, 9(3), 372–381.

    Article  Google Scholar 

  27. Carbonaro, N., et al. (2012, October 21–26). Unobtrusive physiological and gesture wearable acquisition system: A preliminary study on behavioral and emotional correlations. GLOBAL HEALTH, Venice, pp. 88–92.

    Google Scholar 

  28. Sumner, B., Mancuso, C., & Paradiso, R. (2013, July 3–7). Performances evaluation of textile electrodes for EMG remote measurements. 35th annual international conference of the IEEE EMBS Osaka, 6510-Analog Device, available online October 2017. http://www.analog.com

  29. Gallego, J. A., Rocon, E., Roa, J. O., Moreno, J. C., Koutsou, A. D., & Pons, J. L. (2009). On the use of inertial measurement units for real-time quantification of pathological tremor amplitude and frequency. Procedia Chemistry. https://doi.org/10.1016/j.proche.2009.07.304.

  30. Gallego, J.A., Rocon, E., Belda, J. M., & Pons, J. L. (2013). Journal of Neuroengineering Rehabilitation, 10, 36. Published online 15 Apr 2013. https://doi.org/10.1186/1743-0003-10-36 24.

  31. Paradiso, R., & Caldani, L. (2010). Electronic textile platforms for monitoring in a natural environment. Research Journal of Textile and Apparel, 14, 9–21.

    Article  Google Scholar 

  32. Caldani, L., Mancuso, C., & Paradiso, R. (2013). E-textile platform for movement disorder treatment. In J. L. Pons, D. Torricelli, & M. Pajaro (Eds.), Converging clinical and engineering research on neurorehabilitation (pp. 1049–1053). Berlin: Springer.

    Chapter  Google Scholar 

  33. Lister, G. (1977). The hand: Diagnosis and surgical indications. London: Churchill Livingstone.

    Google Scholar 

  34. Mancuso, C., De Toma, G., & Paradiso, R. (2013). Wearable electrogoniometer for knee joint parameters capture. In J. L. Pons, D. Torricelli, & M. Pajaro (Eds.), Converging clinical and engineering research on neurorehabilitation (pp. 1055–1059). Berlin: Springer.

    Chapter  Google Scholar 

  35. Luinge, H., Veltink, P., & Baten, C. (2007). Ambulatory measurement of arm orientation. Journal of Biomechanics, 40, 78–85.

    Article  Google Scholar 

  36. Schepers, H. M., Koopman, H. F. J. M., & Veltink, P. H. (2007). Ambulatory assessment of ankle and foot dynamics. IEEE Transactions on Biomedical Engineering, 54(5), 895–902. https://doi.org/10.1109/TBME.2006.889769.

    Article  Google Scholar 

  37. XSENS MTw. www.xsens.com/en/general/mtw

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Authors and Affiliations

  1. Smartex srl, via Lungo il Ficarello 3, 49100, Prato, PO, Italy

    Rita Paradiso, Gianluca De Toma & Carlo Mancuso

Authors
  1. Rita Paradiso
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  2. Gianluca De Toma
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  3. Carlo Mancuso
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Corresponding author

Correspondence to Rita Paradiso .

Editor information

Editors and Affiliations

  1. Waseda University, Shinjuku, Tokyo, Japan

    Toshiyo Tamura

  2. The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Wenxi Chen

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Paradiso, R., De Toma, G., Mancuso, C. (2018). Smart Textile Suit. In: Tamura, T., Chen, W. (eds) Seamless Healthcare Monitoring. Springer, Cham. https://doi.org/10.1007/978-3-319-69362-0_9

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  • DOI: https://doi.org/10.1007/978-3-319-69362-0_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69361-3

  • Online ISBN: 978-3-319-69362-0

  • eBook Packages: EngineeringEngineering (R0)

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