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
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Empirical Study of Soft Clustering Technique for Determining Click Through Rate in Online Advertising Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Analyzing the Detectability of Harmful Postures for Patient with Hip Prosthesis Based on a Single Accelerometer in Mobile Phone

verfasst von : Kitti Naonueng, Opas Chutatape, Rong Phoophuangpairoj

Erschienen in: Data Management, Analytics and Innovation

Verlag: Springer Singapore

Einloggen

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

search-config
loading …

Abstract

This research studies the use of a single accelerometer inside a smartphone as a sensor to detect those postures that may be risks for patients with hip surgery to dislocate their joints. Various postures were analyzed using Euclidean distances to determine the feasibility to detect eight postures that were harmful. With the mobile phone attached to the affected upper leg, it was found that there was one harmful posture that could not be detected due to its close similarity with a normal posture. Meanwhile, the other two harmful postures, although indistinguishable based on their measured data, were still detectable with the suitably selected threshold. The distance measure analysis is useful as an indicator as to which posture will be near to missing out in the detection process. This will form a guideline for further design of a practical and more robust detecting system.

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 An Approach: Applicability of Existing Heterogeneous Multicore Real-Time Task Scheduling in Commercially Available Heterogeneous Multicore Systems
Nächstes Kapitel Software Development Process Evolution in Malaysian Companies
Literatur
1.
2.
Muro-de-la-Herran, A., Garcia-Zapirain, B., Mendez-Zorrilla, A.: Gait analysis methods: an overview of wearable and non-wearable systems. Highlighting Clin. Appl. Sens. 14(2), 3362–3394 (2014)
3.
Noury, N., Fleury, A., Rumeau, P., Bourke, A. K., Rialle, V., Lundy, J.E.: Fall detection: principles and methods. In: Proceedings of 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBS), pp. 1663–1666 (2007)
4.
Brezmes, T., Gorricho, J.-L., Cotrina, J.: Activity Recognition from Accelerometer Data on a Mobile Phone. Lecture Notes in Computer Science, vol. 5518, pp. 796–799. Springer, Berlin (2009)
5.
Krishnan, N., Panchanathan, S.: Analysis of low resolution accelerometer data for continuous human activity recognition. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3337–3340 (2008)
6.
Long, X., Yin, B., Aarts, R.M.: Single accelerometer-based daily physical activity classification. In: Proceedings 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), pp. 6107–6110 (2009)
7.
Mannini, A., Sabatini, A.M.: Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 10(2), 1154–1175 (2010)CrossRef
8.
Huda, F.A., Tolle, H., Putra, K.P.: Human activity recognition using single accelerometer on smartphone put on user’s head with head-mounted display. Int. J. Adv. Soft Comput. Appl. 9(3), 239–249 (2017)
9.
Mathie, M., Celler, B., Lovell, N., Coster, A.: Classification of basic daily movements using a triaxial accelerometer. Med. Biol. Eng. Comput. 42, 679–687 (2004)CrossRef
10.
Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SIGKDD Explor. Newsl. 12(2), 74–82 (2010)CrossRef
11.
Putra, I.P.R.S., Brusey, J., Gaura, E., Vesilo, R.: An event-triggered machine learning approach for accelerometer-based fall detection. Sensors 18(20), 1–18 (2017)
12.
Gjoreski, H., Luštrek, M., Gams, M.: Accelerometer placement for posture recognition and fall detection. In: Proceedings of International Conference on Intelligent Environments, Nottingham, UK, pp. 47–54 (2011). https://​doi.​org/​10.​1109/​ie.​2011.​11
13.
Khawandi, S., Daya, B., Chauvet, P.: Automated monitoring system for fall detection in the elderly. Int. J. Image Process. 4(5), 476–483 (2010)
14.
Phoophuangpairoj, R.: Frame-based analysis of knee extension exercises using a smartphone accelerometer. In: Proceedings of 13th International Joint Conference on Computer Science and Software Engineering (JCSSE) (2016)
15.
Chutatape, O., Naonueng, K., Phoophuangpairoj, R.: Detection of dangerous postures leading to dislocation of hip prosthesis by a smartphone. In: Proceedings of 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (2017)
16.
17.
Phoophuangpairoj, R.: Developing an accelerometer-based mobile application to aid knee extension exercise. Int. J. Adv. Soft Comput. Appl. 10(2), 132–147 (2018)
Metadaten
Titel
Analyzing the Detectability of Harmful Postures for Patient with Hip Prosthesis Based on a Single Accelerometer in Mobile Phone
verfasst von
Kitti Naonueng
Opas Chutatape
Rong Phoophuangpairoj
Copyright-Jahr
2020
Verlag
Springer Singapore
Buch
Data Management, Analytics and Innovation

Print ISBN: 978-981-329-948-1

Electronic ISBN: 978-981-329-949-8

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-981-32-9949-8_9

Neuer Inhalt

    Bildnachweise