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Biomechanical Biofeedback Systems and Applications

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

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Abstract

Biofeedback is a highly interdisciplinary area of research that combines knowledge of several scientific fields; from medical and social sciences to engineering and natural sciences. Biofeedback systems studied in this book include several elements; one of them is a person, a user of the system that presents its biological component. Other elements of the biofeedback system can be technical devices, persons, or a combination of both.

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References

  • Alahakone AU, Senanayake SA (2009) A real time vibrotactile biofeedback system for improving lower extremity kinematic motion during sports training. In: International conference of soft computing and pattern recognition, 2009. SOCPAR’09. IEEE, pp 610–615, Dec 2009

    Google Scholar 

  • Ali SMR (2013) Behaviour profiling using wearable sensors for pervasive healthcare

    Google Scholar 

  • Andersen LB, Harro M, Sardinha LB, Froberg K, Ekelund U, Brage S, Anderssen SA (2006) Physical activity and clustered cardiovascular risk in children: a cross-sectional study (The European Youth Heart Study). Lancet 368(9532):299–304

    Article  Google Scholar 

  • Andreu-Perez J, Leff DR, Ip HM, Yang GZ (2015) From wearable sensors to smart implants—toward pervasive and personalized healthcare. IEEE Trans Biomed Eng 62(12):2750–2762

    Article  Google Scholar 

  • Baca A, Kornfeind P (2006) Rapid feedback systems for elite sports training. IEEE Pervasive Comput 5(4):70–76

    Article  Google Scholar 

  • Baca A, Dabnichki P, Heller M, Kornfeind P (2009) Ubiquitous computing in sports: a review and analysis. J Sports Sci 27(12):1335–1346

    Article  Google Scholar 

  • Becker BE (2009) Aquatic therapy: scientific foundations and clinical rehabilitation applications. PM&R 1(9):859–872

    Article  Google Scholar 

  • Bilodeau EA, Bilodeau IM, Alluisi EA (1969) Principles of skill acquisition. Academic Press

    Google Scholar 

  • Camomilla V, Bergamini E, Fantozzi S, Vannozzi G (2016). In-field use of wearable magneto-inertial sensors for sports performance evaluation. In: ISBS-conference proceedings archive, vol 33, No 1, May 2016

    Google Scholar 

  • Casamassima F, Ferrari A, Milosevic B, Ginis P, Farella E, Rocchi L (2014) A wearable system for gait training in subjects with Parkinson’s disease. Sensors 14(4):6229–6246

    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 

  • Chambers R, Gabbett TJ, Cole MH, Beard A (2015) The use of wearable microsensors to quantify sport-specific movements. Sports Med 45(7):1065–1081

    Article  Google Scholar 

  • Crowell HP, Milner CE, Hamill J, Davis IS (2010) Reducing impact loading during running with the use of real-time visual feedback. J Orthop Sports Phys Ther 40(4):206–213

    Article  Google Scholar 

  • Dai J, Bai X, Yang Z, Shen Z, Xuan D (2010) Mobile phone-based pervasive fall detection. Pers Ubiquit Comput 14(7):633–643

    Article  Google Scholar 

  • Dozza M, Chiari L, Peterka RJ, Wall C, Horak FB (2011) What is the most effective type of audio-biofeedback for postural motor learning? Gait Posture 34(3):313–319

    Article  Google Scholar 

  • Ebling MR (2016) IoT: from sports to fashion and everything in-between. IEEE Pervasive Comput 4:2–4

    Google Scholar 

  • Eriksson M, Halvorsen KA, Gullstrand L (2011) Immediate effect of visual and auditory feedback to control the running mechanics of well-trained athletes. J Sports Sci 29(3):253–262

    Article  Google Scholar 

  • Feese S, Burscher MJ, Jonas K, Tröster G (2014) Sensing spatial and temporal coordination in teams using the smartphone. Hum-Centric Comput Inf Sci 4(1):1–18 (Springer, Berlin, Heidelberg)

    Google Scholar 

  • Feng Y, Max L (2014) Accuracy and precision of a custom camera-based system for 2-D and 3-D motion tracking during speech and nonspeech motor tasks. J Speech, Lang Hear Res 57(2):426–438

    Article  Google Scholar 

  • Fernandez F, Pallis GC (2014) Opportunities and challenges of the Internet of Things for healthcare: systems engineering perspective. In: 2014 EAI 4th international conference on wireless mobile communication and healthcare (Mobihealth). IEEE, pp. 263–266, Nov 2014

    Google Scholar 

  • Franco C, Fleury A, Guméry PY, Diot B, Demongeot J, Vuillerme N (2013) iBalance-ABF: a smartphone-based audio-biofeedback balance system. IEEE Trans Biomed Eng 60(1):211–215

    Article  Google Scholar 

  • Giblin G, Tor E, Parrington L (2016) The impact of technology on elite sports performance. Sensoria: J Mind, Brain Cult 12(2)

    Google Scholar 

  • Giggins OM, Persson UM, Caulfield B (2013) Biofeedback in rehabilitation. J Neuroeng Rehabil 10(1):60

    Article  Google Scholar 

  • Giggins OM, Sweeney KT, Caulfield B (2014) Rehabilitation exercise assessment using inertial sensors: a cross-sectional analytical study. J Neuroeng Rehabil 11(1):158

    Article  Google Scholar 

  • Gravenhorst F, Muaremi A, Bardram J, Grünerbl A, Mayora O, Wurzer G, …, Tröster G (2014) Mobile phones as medical devices in mental disorder treatment: an overview. Pers Ubiquit Comput 1–19

    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 

  • Gruwsved Å, Söderback I, Fernholm C (1996) Evaluation of a vocational training programme in primary health care rehabilitation: a case study. Work 7(1):47–61

    Article  Google Scholar 

  • Hanson S, Jones A (2015) Is there evidence that walking groups have health benefits? A systematic review and meta-analysis. Br J Sports Med 49(11):710–715

    Article  Google Scholar 

  • Hardegger M, Roggen D, Tröster G (2015) 3D ActionSLAM: wearable person tracking in multi-floor environments. Pers Ubiquit Comput 19(1):123–141

    Article  Google Scholar 

  • Hiremath S, Yang G, Mankodiya K (2014) Wearable Internet of Things: concept, architectural components and promises for person-centered healthcare. In: 2014 EAI 4th international conference on wireless mobile communication and healthcare (Mobihealth), Nov 2014

    Google Scholar 

  • Huang H, Wolf SL, He J (2006) Recent developments in biofeedback for neuromotor rehabilitation. J Neuroeng Rehabil 3(1):11

    Article  Google Scholar 

  • iSen, Inertial motion capture (2018) https://www.stt-systems.com/products/inertial-motion-capture/isen/. Accessed 23 June 2018

  • Josefsson T (2002) U.S. Patent No. 6,437,820. U.S. Patent and Trademark Office, Washington, DC

    Google Scholar 

  • Kim J, Kim J, Lee D, Chung KY (2014) Ontology driven interactive healthcare with wearable sensors. Multimed Tools Appl 71(2):827–841

    Article  Google Scholar 

  • Kirby R (2009) Development of a real-time performance measurement and feedback system for alpine skiers. Sports Technol 2(1–2):43–52

    Article  Google Scholar 

  • Kos A, Umek A (2018) Wearable sensor devices for prevention and rehabilitation in healthcare: swimming exercise with real-time therapist feedback. IEEE Internet Things J. https://doi.org/10.1109/jiot.2018.2850664

  • Kos A, Milutinović V, Umek A (2018) Challenges in wireless communication for connected sensors and wearable devices used in sport biofeedback applications. Future Gener Comput Syst

    Google Scholar 

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

    Article  Google Scholar 

  • Kunze K, Minamizawa K, Lukosch S, Inami M, Rekimoto J (2017) Superhuman sports: applying human augmentation to physical exercise. IEEE Pervasive Comput 16(2):14–17

    Article  Google Scholar 

  • Lauber B, Keller M (2014) Improving motor performance: Selected aspects of augmented feedback in exercise and health. Eur J Sport Sci 14(1):36–43

    Article  Google Scholar 

  • Lieberman J, Breazeal C (2007) Development of a wearable vibrotactile feedback suit for accelerated human motor learning. In: 2007 IEEE international conference on robotics and automation. IEEE, pp 4001–4006, Apr 2007

    Google Scholar 

  • Liebermann DG, Katz L, Hughes MD, Bartlett RM, McClements J, Franks IM (2002) Advances in the application of information technology to sport performance. J Sports Sci 20(10):755–769

    Article  Google Scholar 

  • Lightman K (2016) Silicon gets sporty. IEEE Spectr 53(3):48–53

    Article  Google Scholar 

  • Lohne-Seiler H, Hansen BH, Kolle E, Anderssen SA (2014) Accelerometer-determined physical activity and self-reported health in a population of older adults (65–85 years): a cross-sectional study. BMC Public Heal 14(1):284

    Article  Google Scholar 

  • McGrath MJ, Scanaill CN (2013) Wellness, fitness, and lifestyle sensing applications. In: Sensor technologies. Apress, Berkeley, CA, pp 217–248

    Google Scholar 

  • Metcalf D, Milliard ST, Gomez M, Schwartz M (2016) Wearables and the internet of things for health: wearable, interconnected devices promise more efficient and comprehensive health care. IEEE Pulse 7(5):35–39

    Article  Google Scholar 

  • Nagle EF, Sanders ME, Franklin BA (2017) Aquatic high intensity interval training for cardiometabolic health: benefits and training design. Am J Lifestyle Med 11(1):64–76

    Article  Google Scholar 

  • Neiva HP, Marques MC, Travassos BF, Marinho DA (2017) Wearable Technology and aquatic activities: a review. Motricidade 13(1):219

    Google Scholar 

  • OptiTrack (2018) http://optitrack.com/hardware/. Accessed 30 June 2018

  • Park S, Jayaraman S (2003) Enhancing the quality of life through wearable technology. IEEE Eng Med Biol Mag 22(3):41–48

    Article  Google Scholar 

  • Pernek I, Hummel KA, Kokol P (2013) Exercise repetition detection for resistance training based on smartphones. Pers Ubiquit Comput 17(4):771–782

    Article  Google Scholar 

  • Prins J, Cutner D (1999) Aquatic therapy in the rehabilitation of athletic injuries. Clin Sports Med 18(2):447–461

    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 10 June 2018

  • Ruiz JR, Ortega FB, Rizzo NS, Villa I, Hurtig-Wennlöf A, Oja L, Sjöström M (2007) High cardiovascular fitness is associated with low metabolic risk score in children: the European Youth Heart Study. Pediatr Res 61(3):350–355

    Article  Google Scholar 

  • Saintoyant PY, Mahonen P (2006) U.S. Patent Application No. 11/030,217

    Google Scholar 

  • Schaffert N, Mattes K, Effenberg AO (2010) A sound design for acoustic feedback in elite sports. In: Auditory display. Springer, Berlin, Heidelberg, pp 143–165

    Chapter  Google Scholar 

  • Schneider J, Börner D, Van Rosmalen P, Specht M (2015) Augmenting the senses: a review on sensor-based learning support. Sensors 15(2):4097–4133

    Article  Google Scholar 

  • Seneviratne S, Hu Y, Nguyen T, Lan G, Khalifa S, Thilakarathna K, …, Seneviratne A (2017) A survey of wearable devices and challenges. IEEE Commun Surv Tutor 19(4):2573–2620

    Article  Google Scholar 

  • Sigrist R, Rauter G, Riener R, Wolf P (2013) Augmented visual, auditory, haptic, and multimodal feedback in motor learning: a review. Psychon Bull Rev 20(1):21–53

    Article  Google Scholar 

  • Silva ASM (2014) Wearable sensors systems for human motion analysis: sports and rehabilitation

    Google Scholar 

  • Singh R, Stringer H, Drew T, Evans C, Jones RS (2015) Swimming breaststroke after total hip replacement; are we sending the correct message. J Arthritis 4(147):2

    Google Scholar 

  • The Xsens wearable motion capture solutions (2018) https://www.xsens.com/products/xsens-mvn/. Accessed 10 June 2018

  • Tomažič S (2006) Quality of life: a challenge for engineers?. In: International conference on advances in the internet, processing, systems and interdisciplinary research, Montreal, New York, Boston

    Google Scholar 

  • Tracklab, Inertial Motion Capture Systems (2018) https://tracklab.com.au/inertial-motion-capture-systems/. Accessed 23 June 2018

  • Tronconi M (2013) MEMS and Sensors are the key enablers of Internet of Things. STMicroelectronics: Geneva, Switzerland

    Google Scholar 

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

    Article  Google Scholar 

  • Umek A, Kos A (2018) Smart equipment design challenges for real time feedback support in sport. Facta Universitatis, Series: Mechanical Engineering

    Google Scholar 

  • Umek A, Tomažič S, Kos A (2015) Wearable training system with real-time biofeedback and gesture user interface. Pers Ubiquit Comput 19(7):989–998

    Article  Google Scholar 

  • Varkey JP, Pompili D, Walls TA (2012) Human motion recognition using a wireless sensor-based wearable system. Pers Ubiquit Comput 16(7):897–910

    Article  Google Scholar 

  • Vicon, Camera Systems (2018) https://www.vicon.com/products/camera-systems. Accessed 23 June 2018

  • Vogt K, Pirró D, Kobenz I, Höldrich R, Eckel G (2010) PhysioSonic-evaluated movement sonification as auditory feedback in physiotherapy. In: Auditory display. Springer, Berlin, Heidelberg, pp 103–120

    Chapter  Google Scholar 

  • von der Grün T, Franke N, Wolf D Witt N, Eidloth A (2011) A real-time tracking system for football match and training analysis. In: Microelectronic systems. Springer, Berlin, Heidelberg, pp 199–212

    Chapter  Google Scholar 

  • Wei Y, Yan H, Bie R, Wang S, Sun L (2014) Performance monitoring and evaluation in dance teaching with mobile sensing technology. Pers Ubiquit Comput 18(8):1929–1939

    Article  Google Scholar 

  • Williamson J, Liu Q, Lu F, Mohrman W, Li K, Dick R, Shang L (2015) Data sensing and analysis: challenges for wearables. In: 2015 20th Asia and South Pacific design automation conference (ASP-DAC). IEEE, pp 136–141, Jan 2015

    Google Scholar 

  • Windolf M, Götzen N, Morlock M (2008) Systematic accuracy and precision analysis of video motion capturing systems—exemplified on the Vicon-460 system. J Biomech 41(12):2776–2780

    Article  Google Scholar 

  • Wong C, Zhang ZQ, Lo B, Yang GZ (2015) Wearable sensing for solid biomechanics: a review. IEEE Sens J 15(5):2747–2760

    Google Scholar 

  • Xia F, Hsu CH, Liu X, Liu H, Ding F, Zhang W (2013) The power of smartphones. Multimed Syst 21(1):87–101

    Article  Google Scholar 

  • Yilmaz I, Yanardag M, Birkan B, Bumin G (2004) Effects of swimming training on physical fitness and water orientation in autism. Pediatr Int 46(5):624–626

    Article  Google Scholar 

  • Zenith, Smartphone penetration (2018) https://www.zenithmedia.com/smartphone-penetration-reach-66-2018/. Accessed 23 June 2018

  • Zhang S, McCullagh P, Zhang J, Yu T (2014) A smartphone based real-time daily activity monitoring system. Clust Comput 17(3):711–721

    Article  Google Scholar 

  • Zhang Y, Xhafa F, Ruiz C, Yao L (2018) Special section editorial: wearable sensor signal processing for smart health. Smart Heal 5–6:1–3 (2018). https://doi.org/10.1016/j.smhl.2018.03.004

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia

    Anton Kos

  2. Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia

    Anton Umek

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  1. Anton Kos
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Kos, A., Umek, A. (2018). Introduction. In: Biomechanical Biofeedback Systems and Applications. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-91349-0_1

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  • DOI: https://doi.org/10.1007/978-3-319-91349-0_1

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  • Online ISBN: 978-3-319-91349-0

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