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Performance Limitations of Biofeedback System Technologies

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Biomechanical Biofeedback Systems and Applications

Part of the book series: Human–Computer Interaction Series ((HCIS))

Abstract

Use of technology in biofeedback systems has a great potential, but it also faces many limitations and challenges. Technologies used in biofeedback systems and applications are numerous and diverse; therefore, only some of the most challenging in terms of their properties and limitations are studied in this chapter. MEMS inertial sensors and wireless communication are without doubt good examples of technologies that are requiring more attention. MEMS inertial sensors are studied through their properties in terms of their inaccuracies induced by biases, noise, and bias variations. Their bias compensation efficiency is tested against the highly accurate professional optical system and results of mass measurements of inertial sensors integrated into smartphones are presented. In addition to sensing and processing, communication capabilities of biofeedback systems are also of great importance for their correct and timely operation. Guidelines for the most appropriate choice of wireless communication technologies for different implementations of biofeedback systems and applications are given at the end of this chapter.

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References

  • Adame T, Bel A, Bellalta B, Barcelo J, Oliver M (2014) IEEE 802.11 AH: the WiFi approach for M2 M communications. IEEE Wirel Commun 21(6):144–152

    Article  Google Scholar 

  • Aggarwal P, Syed Z, Niu X, El-Sheimy N (2006) Cost-effective testing and calibration of low cost MEMS sensors for integrated positioning, navigation and mapping systems. In: Proceedings of XXIII FIG congress, Munich, Germany, vol 813

    Google Scholar 

  • Aggarwal P, Syed Z, Niu X, El-Sheimy N (2008) A standard testing and calibration procedure for low cost MEMS inertial sensors and units. J Navig 61(2):323–336

    Article  Google Scholar 

  • Allan Variance (2003) http://www.allanstime.com/AllanVariance/. Accessed 30 June 2018

  • Allan DW (1966) Statistics of atomic frequency standards. Proc IEEE 54(2):221–230

    Article  Google Scholar 

  • Ayub K, Zagurskis V (2015) Technology implications of UWB on wireless sensor network-a detailed survey. Int J Commun Netw Inf Secur (IJCNIS) 7(3)

    Google Scholar 

  • Baños-Gonzalez V, Afaqui MS, Lopez-Aguilera E, Garcia-Villegas E (2016) IEEE 802.11 ah: a technology to face the IoT challenge. Sensors 16(11), 1960

    Article  Google Scholar 

  • Cao H, Leung V, Chow C, Chan H (2009) Enabling technologies for wireless body area networks: a survey and outlook. IEEE Commun Mag 47(12)

    Article  Google Scholar 

  • Cavallari R, Martelli F, Rosini R, Buratti C, Verdone R (2014) A survey on wireless body area networks: Technologies and design challenges. IEEE Commun Surv Tutor 16(3):1635–1657

    Article  Google Scholar 

  • Chen M, Gonzalez S, Vasilakos A, Cao H, Leung VC (2011) Body area networks: a survey. Mob Netw Appl 16(2):171–193

    Article  Google Scholar 

  • Deslise JJ (2015) What’s the difference between IEEE 802.11 af and 802.11 ah? Microw RF 54:69–72

    Google Scholar 

  • Dixon-Warren SJ (2010) Motion sensing in the iPhone 4: MEMS Accelerometer. MEMS Journal

    Google Scholar 

  • Dixon-Warren SJ (2011) Motion sensing in the iPhone 4: MEMS gyroscope. MEMS Journal

    Google Scholar 

  • El-Diasty M, Pagiatakis S (2008) Calibration and stochastic modelling of inertial navigation sensor errors. J Glob Position Syst 7(2):170–182

    Article  Google Scholar 

  • El-Sheimy N, Hou H, Niu X (2008) Analysis and modeling of inertial sensors using Allan variance. IEEE Trans Instrum Meas 57(1):140–149

    Article  Google Scholar 

  • Grewal M, Andrews A (2010) How good is your gyro [ask the experts]. IEEE Control Syst 30(1):12–86

    Article  Google Scholar 

  • Hämäläinen M, Paso T, Mucchi L, Girod-Genet M, Farserotu J, Tanaka H, … Ismail LN (2015) ETSI TC SmartBAN: overview of the wireless body area network standard. In: 2015 9th international symposium on medical information and communication technology (ISMICT), pp 1–5. IEEE

    Google Scholar 

  • Hongwei S, Yuli L, Guangfeng C (2010) Relations between the Standard variance and the Allan variance. In: 2010 international conference on computational and information sciences, pp 66–67. IEEE

    Google Scholar 

  • Human Reaction Time (1970–1979) The Great Soviet Encyclopedia, 3rd edn

    Google Scholar 

  • IEEE (1999) IEEE standard specification format guide and test procedure for linear, single-axis, non-gyroscopic accelerometers

    Google Scholar 

  • Jiang C, Xue L, Chang H, Yuan G, Yuan W (2012) Signal processing of MEMS gyroscope arrays to improve accuracy using a 1st order markov for rate signal modeling. Sensors 12(2):1720–1737

    Article  Google Scholar 

  • Kos A, Milutinović V, Umek A (2018) Challenges in wireless communication for connected sensors and wearable devices used in sport biofeedback applications. In: Future generation computer systems

    Google Scholar 

  • Kos A, TomaĹľiÄŤ S, Umek A (2016a) Suitability of smartphone inertial sensors for real-time biofeedback applications. Sensors 16(3):301

    Article  Google Scholar 

  • Kos A, TomaĹľiÄŤ S, Umek A (2016b) Evaluation of smartphone inertial sensor performance for cross-platform mobile applications. Sensors 16(4):477

    Article  Google Scholar 

  • Kwak KS, Ullah S, Ullah N (2010). An overview of IEEE 802.15. 6 standard. In: 2010 3rd international symposium on applied sciences in biomedical and communication technologies (ISABEL), pp 1–6. IEEE

    Google Scholar 

  • Land DV, Levick AP, Hand JW (2007) The use of the Allan deviation for the measurement of the noise and drift performance of microwave radiometers. Meas Sci Technol 18(7):1917

    Article  Google Scholar 

  • Leland RP (2005) Mechanical-thermal noise in MEMS gyroscopes. IEEE Sens J 5(3):493–500

    Article  Google Scholar 

  • Lin JR, Talty T, Tonguz OK (2015) On the potential of bluetooth low energy technology for vehicular applications. IEEE Commun Mag 53(1):267–275

    Article  Google Scholar 

  • Liu M (2013) A study of mobile sensing using smartphones. Int J Distrib Sens Netw 9(3):272916

    Article  Google Scholar 

  • Looney M (2010) A simple calibration for MEMS gyroscopes. EDN (Electri Des News) 55(9):21

    Google Scholar 

  • Mohd-Yasin F, Korman CE, Nagel DJ (2003) Measurement of noise characteristics of MEMS accelerometers. Solid-State Electron 47(2):357–360

    Article  Google Scholar 

  • Movassaghi S, Abolhasan M, Lipman J, Smith D, Jamalipour A (2014) Wireless body area networks: A survey. IEEE Commun Surv Tutor 16(3):1658–1686

    Article  Google Scholar 

  • Nilsson L (2011) QTM Real-time Server Protocol Documentation Version 1.9. http://qualisys.github.io/rt-protocol/. Accessed 10 Sept 2015

  • Prikhodko IP, Trusov AA, Shkel AM (2013) Compensation of drifts in high-Q MEMS gyroscopes using temperature self-sensing. Sens Actuators, A 201:517–524

    Article  Google Scholar 

  • Pyattaev A, Johnsson K, Andreev S, Koucheryavy Y (2015) Communication challenges in high-density deployments of wearable wireless devices. IEEE Wirel Commun 22(1):12–18

    Article  Google Scholar 

  • Qualisys, Motion Capture System (2018). http://www.qualisys.com. Accessed 20 June 2018

  • Shaeffer DK (2013) MEMS inertial sensors: A tutorial overview. IEEE Commun Mag 51(4):100–109

    Article  Google Scholar 

  • Siddiqui SA, Zhang Y, Lloret J, Song H, Obradovic Z (2018) Pain-free blood glucose monitoring using wearable sensors: recent advancements and future prospects. In: IEEE reviews in biomedical engineering

    Google Scholar 

  • ST Microelectronics (2009) M.E.M.S. digital output motion sensor ultra low-power high performance 3-Axes “Nano” Accelerometer, LIS331DLH Specifications

    Google Scholar 

  • ST Microelectronics (2010) MEMS motion sensor: ultra-stable three-axis digital output gyroscope. L3G4200D Specifications, ST Microelectronics, Geneva, Switzerland

    Google Scholar 

  • ST Microelectronics (2011) Everything about STMicroelectronics’3-Axis Digital MEMS Gyroscopes, TA0343, Technical article. ST Microelectronics. July, 36

    Google Scholar 

  • StanÄŤin S, TomaĹľiÄŤ S (2014) Time-and computation-efficient calibration of MEMS 3D accelerometers and gyroscopes. Sensors 14(8):14885–14915

    Article  Google Scholar 

  • Stockwell W (2004) Bias stability measurement: Allan variance. http://www.moog-cross-bow.com/Literature/Application_Notes_Papers/Gyro_Bias_Stability_Measurement_using_Allan_Variance.pdf. Accessed 26 Dec 2015

  • Umek A, Kos A (2016a). The role of high performance computing and communication for real-time biofeedback in sport. Math Probl Eng 2016

    Google Scholar 

  • Umek A, Kos A (2016b) Validation of smartphone gyroscopes for mobile biofeedback applications. Pers Ubiquit Comput 20(5):657–666

    Article  Google Scholar 

  • Wang CX, Haider F, Gao X, You XH, Yang Y, Yuan D, … Hepsaydir E (2014) Cellular architecture and key technologies for 5G wireless communication networks. IEEE Commun Mag 52(2), 122–130

    Article  Google Scholar 

  • Weinberg H (2011) Gyro mechanical performance: the most important parameter. Technical Article MS-2158

    Google Scholar 

  • Woodman OJ (2007) An introduction to inertial navigation (No. UCAM-CL-TR-696). University of Cambridge, Computer Laboratory

    Google Scholar 

  • Zhang Y, Sun L, Song H, Cao X (2014) Ubiquitous WSN for healthcare: recent advances and future prospects. IEEE Internet Things J 1(4):311–318

    Article  Google Scholar 

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Kos, A., Umek, A. (2018). Performance Limitations of Biofeedback System Technologies. In: Biomechanical Biofeedback Systems and Applications. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-91349-0_6

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  • DOI: https://doi.org/10.1007/978-3-319-91349-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91348-3

  • Online ISBN: 978-3-319-91349-0

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