Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
How to Cross the Border from R to D? The Example of Conception of New Medical Devices Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Real-Time Fuzzy Monitoring of Sitting Posture: Development of a New Prototype and a New Posture Classification Algorithm to Detect Postural Transitions

verfasst von : Leonardo Martins, Bruno Ribeiro, Hugo Pereira, Rui Almeida, Jéssica Costa, Cláudia Quaresma, Adelaide Jesus, Pedro Vieira

Erschienen in: Biomedical Engineering Systems and Technologies

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In a previous work, a chair prototype was used to detect 11 standardized siting postures of users, using just 8 air bladders (4 in the chair’s seat and 4 in the backrest) and one pressure sensor for each bladder. In this paper we describe the development of a new prototype, which is able to classify 12 standard postures with an overall score of 80.9 % (using a Neural Network Algorithm). We tested how this Algorithm worked during postural transitions (frontal and lateral flexion) and in intermediate postures, identifying some limitation of this Algorithm. This prompted the development of a Posture Classification Algorithm based on Fuzzy Logic and is able to determine if the user is adopting a good or a bad posture for specific time periods, using as input the Centre of Pressure, the Posture Adoption Time and the Posture Output from the existing Neural Network Algorithm. This newly developed Classification Algorithms is advancing the development of new Posture Correction Algorithms based on Fuzzy Actuators.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Towards a Safer and More Optimal Treatment of the Supracondylar Humerus Fracture
Nächstes Kapitel TogetherActive - Key Concepts and Usability Study
Literatur
1.
Owen, N., Sugiyama, T., Eakin, E.E., Gardiner, Pa, Tremblay, M.S., Sallis, J.F.: Adults’ sedentary behavior determinants and interventions. Am. J. Prev. Med. 41, 189–196 (2011)CrossRef
2.
Chau, J.Y., Ploeg, H.P.Van, Der, Uffelen, Van, J.G.Z., Wong, J., Riphagen, I., Healy, G.N., Gilson, N.D., Dunstan, D.W., Bauman, A.E., Owen, N., Brown, W.J.: Are workplace interventions to reduce sitting effective ? Sys. Rev. Prev. Med. (Baltim) 51, 352–356 (2010)CrossRef
3.
Ramdan, N.S.A., Hashim, A.Y.B., Kamat, S.R., Mokhtar, M.N.A., Asmai, S.A.: On lower-back pain and its consequence to productivity. J. Ind. Intell. Inf. 2, 83–87 (2014)
4.
Punnett, L., Wegman, D.H.: Work-related musculoskeletal disorders: the epidemiologic evidence and the debate. J. Electromyogr. Kinesiol. 14, 13–23 (2004)CrossRef
5.
Hartvigsen, J., Leboeuf-yde, C., Lings, S., Corder, E.H.: Is sitting-while-at-work associated with low back pain? A systematic, critical literature review. Scand. J. Public Health 28, 230–239 (2000)
6.
Roffey, D.M., Wai, E.K., Bishop, P., Kwon, B.K., Dagenais, S.: Causal assessment of occupational sitting and low back pain: results of a systematic review. Spine J. 10, 252–261 (2010)CrossRef
7.
Todd, A.I., Bennett, A.I., Christie., C.J.: Physical implications of prolonged sitting in a confined posture-a literature review. Ergon. SA J. Ergon. Soc. South Africa Spec. Ed. 19, 7–21 (2007)
8.
Van Dieën, J.H., De Looze, M.P., Hermans, V.: Effects of dynamic office chairs on trunk kinematics, trunk extensor EMG and spinal shrinkage. Ergonomics 44, 739–750 (2001)CrossRef
9.
Cagnie, B., Danneels, L., Van Tiggelen, D., De Loose, V., Cambier, D.: Individual and work related risk factors for neck pain among office workers: a cross sectional study. Eur. Spine J. 16, 679–686 (2007)CrossRef
10.
Ariëns, G.A., Bongers, P.M., Douwes, M., Miedema, M.C., Hoogendoorn, W.E., van der Wal, G., Bouter, L.M., van Mechelen, W.: Are neck flexion, neck rotation, and sitting at work risk factors for neck pain? Results of a prospective cohort study. Occup. Environ. Med. 58, 200–207 (2001)CrossRef
11.
Juul-Kristensen, B., Søgaard, K., Strøyer, J., Jensen, C.: Computer users’ risk factors for developing shoulder, elbow and back symptoms. Scand. J. Work Environ. Health 30, 390–398 (2004)CrossRef
12.
Adams, M., Hutton, W.: The effect of posture on diffusion into lumbar intervertebral discs. J. Anat. 147, 121–134 (1986)
13.
Kingma, I., van Dieën, J.H., Nicolay, K., Maat, J.J., Weinans, H.: Monitoring water content in deforming intervertebral disc tissue by finite element analysis of MRI data. Magn. Reson. Med. 44, 650–654 (2000)CrossRef
14.
O’Sullivan, K., O’Sullivan, P., O’Sullivan, L., Dankaerts, W.: What do physiotherapists consider to be the best sitting spinal posture? Man. Ther. 17, 432–437 (2012)CrossRef
15.
Pynt, J., Higgs, J., Mackey, M.: Seeking the optimal posture of the seated lumbar spine. Physiother. Theory Pract. 17, 5–21 (2001)CrossRef
16.
Lis, A.M., Black, K.M., Korn, H., Nordin, M.: Association between sitting and occupational LBP. Eur. Spine J. 16, 283–298 (2007)CrossRef
17.
Ellegast, R.P., Kraft, K., Groenesteijn, L., Krause, F., Berger, H., Vink, P.: Comparison of four specific dynamic office chairs with a conventional office chair: impact upon muscle activation, physical activity and posture. Appl. Ergon. 43, 296–307 (2012)CrossRef
18.
Zhu, M., Mart, A.M., Tan, H.Z.: Template-based recognition of static sitting postures. In: Proceedings of The Workshop on Computer Vision and Pattern Recognition for Human Computer Interaction, held at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2003), pp. 1–6. IEEE Computer Society, Madison (2003)
19.
Forlizzi, J., Disalvo, C., Zimmerman, J., Mutlu, B., Hurst, A.: The SenseChair : the lounge chair as an intelligent assistive device for elders. In: DUX 2005 Proceedings of the 2005 Conference on Designing for User eXperience, p. Article No. 31 (2005)
20.
Tan, H.Z., Slivovsky, L.A., Member, S., Pentland, A.: A sensing chair using pressure distribution sensors. IEEE/ASME Trans. Mechatron. 6, 261–268 (2001)CrossRef
21.
Zheng, Y., Morrell, J.: A vibrotactile feedback approach to posture guidance. In: IEEE Haptics Symposium, pp. 351–358 (2010)
22.
Schrempf, A., Schossleitner, G., Minarik, T., Haller, M., Gross, S.: PostureCare - towards a novel system for posture monitoring and guidance. In: 18th World Congress of the International Federation of Automatic Control (IFAC), pp. 593–598 (2011)
23.
Mutlu, B., Krause, A., Forlizzi, J., Guestrin, C., Hodgins, J.: Robust, low-cost, non-intrusive sensing and recognition of seated postures. In: UIST 2007 Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology, pp. 149–158 (2007)
24.
Daian, I., Ruiten, A.M. Van, Visser, A., Zubic, S.: Sensitive chair : a force sensing chair with multimodal real-time feedback via agent. In: ECCE 2007 Proceedings of the 14th European Conference on Cognitive Ergonomics: Invent! Explore!, pp. 163–166 (2007)
25.
Griffiths, E., Saponas, T.S.: Health chair : implicitly sensing heart and respiratory rate. In: UbiComp 2014 Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 661–671 (2014)
26.
Faudzi, A., Athif, M., Suzumori, K., Wakimoto, S.: Development of an intelligent chair tool system applying new intelligent pneumatic actuators. Adv. Robot. 24, 1503–1528 (2010)CrossRef
27.
Goossens, R.H.M., Netten, M.P., van der Doelen, L.H.M.: An office chair to influence the sitting behavior of office workers 41, 2086–2088 (2012)
28.
Palumbo, F., Ullberg, J., Stimec, A., Furfari, F., Karlsson, L., Coradeschi, S.: Sensor network infrastructure for a home care monitoring system. Sens. (Basel) 14, 3833–3860 (2014)CrossRef
29.
Martins, L., Lucena, R., Almeida, R., Belo, J., Quaresma, C., Jesus, A., Vieira, P.: Intelligent chair sensor: classification and correction of sitting posture. Int. J. Syst. Dyn. Appl. 3, 65–80 (2014)
30.
Vergara, M., Page, A.: System to measure the use of the backrest in sitting-posture office tasks. Appl. Ergon. 31, 247–254 (2000)CrossRef
31.
Paliwal, M., Kumar, U.A.: Neural networks and statistical techniques: a review of applications. Expert Syst. Appl. 36, 2–17 (2009)CrossRef
32.
Kar, S., Das, S., Ghosh, P.K.: Applications of neuro fuzzy systems: A brief review and future outline. Appl. Soft Comput. 15, 243–259 (2014)CrossRef
33.
International Organization for Standardization: Ergonomics—Evaluation of static working postures (2000)
34.
Fleiss, J.L.: Reliability of measurement. In: The Design and Analysis of Clinical Experiments, pp. 1–32 (1986)
35.
Hobson, D.A.: Comparative effects of posture on pressure and shear at the body-seat interface. J. Rehabil. Res. Dev. 29, 21 (1992)CrossRef
36.
James, F.: Statistical Methods in Experimental Physics. World Scientific, Singapore (2006)MATHCrossRef
37.
Mamdani, E.H.: Control & science application of fuzzy algorithms for control of simple dynamic plant. Electr. Eng. Proc. Inst. 121, 1585–1588 (1974)CrossRef
Metadaten
Titel
Real-Time Fuzzy Monitoring of Sitting Posture: Development of a New Prototype and a New Posture Classification Algorithm to Detect Postural Transitions
verfasst von
Leonardo Martins
Bruno Ribeiro
Hugo Pereira
Rui Almeida
Jéssica Costa
Cláudia Quaresma
Adelaide Jesus
Pedro Vieira
Copyright-Jahr
2015
Buch
Biomedical Engineering Systems and Technologies

Print ISBN: 978-3-319-27706-6

Electronic ISBN: 978-3-319-27707-3

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-27707-3_26

Premium Partner

    Bildnachweise