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A low-cost embedded inertial measurement system for the monitoring of human movement

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents a part of a prototype system for the monitoring of selected vital functions of humans and some preliminary results obtained from the device using implemented algorithm. The system consists of such essential modules like a microcontroller board, an inertial measurement unit and additional sensors. The main task of the device is human movement monitoring and detecting selected anomalies, e.g. fall or fainting. At the first stage, the movement classification was considered. The main movement type are walking, running and selected variants of transitions between different phases like standing up or going downstairs. The determining of the movement is based on the intuitive algorithm using raw data from accelerometers complemented by sensors like barometer and heart rate monitor. The algorithm utilizes automated multiscale-based peak detection and wavelet transform energy calculations. Finally, some further work directions and development possibilities are discussed.
Wydawca
Rocznik
Strony
542--544
Opis fizyczny
Bibliogr. 9 poz., rys., schem., tab., wzory
Twórcy
  • West Pomeranian University of Technology Szczecin, Faculty of Computer Science and Iinformation Technology, 52 Żołnierska Str., 71-210 Szczecin
autor
  • West Pomeranian University of Technology Szczecin, Faculty of Computer Science and Iinformation Technology, 52 Żołnierska Str., 71-210 Szczecin
Bibliografia
  • [1] AltIMU product page, https://www.pololu.com/product/2470.
  • [2] DeVaul R., Dunn S.: Real-Time Motion Classification for Wearable Computing Applications. Project paper, 2001, http://www.media. mit.edu/wearables/mithril/realtime.pdf
  • [3] Fortune E., Lugade V., Kaufman K.: Posture and movement classification: the comparison of tri-axial accelerometer numbers and anatomical placement. Journal of Biomechanical Engineering, Vol. 136, May, 2014, pp. 0510031–0510038, doi: 10.1115/1.4026230.
  • [4] Kou A. D.: A mechanical analysis of force distribution between redundant, multiple degree-of-freedom actuators in the human: Implications for the central nervous system, in Human Movement Science, No. 13, pp. 635-663, Elsevier, 1994.
  • [5] Kwapisz J., Weiss G., Moore S.: Activity Recognition using Cell Phone Accelerometers. ACM SIGKDD Explorations Newsletter, Vol. 12, Issue 2, December 2010, pp. 74-82.
  • [6] Lockhart T., Soangra R., Zhang J., Wu X.: Wavelet based automated postural event detection and activity classification with single IMU (TEMPO). Biomed Sci Instrum. 2013, Vol. 49, pp. 224-233.
  • [7] Mitchell E., Mongahan D., O’Connor N.: Classification of Sporting Activities Using Smartphone Accelerometers. Sensors, Vol. 13(4), pp. 5317-5337, 2013, doi: 10.3390/s130405317.
  • [8] Scholkmann F., Boss J., Wolf M.: An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals, Algorithms, vol. 5, issue 4, pp. 588-603, doi:10.3390/ a5040588,
  • [9] Teensy 3.1 product page, https://www.pjrc.com/teensy/teensy31.html.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e28d1b6d-9625-4443-89f7-af2245d03145
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