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2017, vol. 27, br. 27, str. 1-9
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Korišćenje funkcije gustine verovatnoće u postupku za prepoznavanje tipa fizičke vežbe
Using probability density function in the procedure for recognition of the type of physical exercise
aUniverzitet u Beogradu, Elektrotehnički institut 'Nikola Tesla' + Univerzitet u Beogradu, Elektrotehnički fakultet bUniverzitet u Beogradu, Elektrotehnički institut 'Nikola Tesla'
e-adresa: nikola.cakic@ieent.org
Sažetak
U radu je prikazan postupak za prepoznavanje fizičkih vežbi, korišćenjem samo troosnog akcelerometra pametnog telefona. Telefon se nalazi nefiksiran u džepu vežbača. Analizirane su vežbe za jačanje mišića nogu iz stojećeg položaja: čučanj, iskorak i podizanje kolena. Sve vežbe su rađene nogom u čijem džepu se nalazi senzor ubrzanja korišćenog mobilnog telefona. Da bi se testirao predloženi postupak, nasumice je odabrana vežba podizanje kolena koja se obavlja suprotnom nogom od one u čijem se džepu nalazi mobilni telefon. Filtriranje sirovih signala ubrzanja je postignutno korišćenjem Batervortovog filtra propusnika niskih učestanosti (desetog reda). Filtrirani signali svake od osa akcelerometra su opisani pomoću tri deskriptora. Nakon izračunavanja deskriptora signala, za svaki deskriptor je formirana funkcija gustine verovatnoće. Postupak za prepoznavanje vežbi se obavlja online unutar Android aplikacije pametnog telefona. Signali dve muške i dve ženske osobe su poslužili kao referenca za prepoznavanje vežbi. Uspešnost prepoznavanja je 94,22% za prepoznavanje tri vežbe, odnosno 85,33% za prepoznavanje sve četiri razmatrane vežbe.
Abstract
This paper presents a method for recognition of physical exercises, using only a triaxial accelerometer of a smartphone. The smartphone itself is free to move inside subject's pocket. Exercises for leg muscle strengthening from subject's standing position squat, right knee rise and lunge with right leg were analyzed. All exercises were performed with the accelerometric sensor of a smartphone placed in the pocket next to the leg used for exercises. In order to test the proposed recognition method, the knee rise exercise of the opposite leg with the same position of the sensor was randomly selected. Filtering of the raw accelerometric signals was carried out using Butterworth tenth-order low-pass filter. The filtered signals from each of the three axes were described using three signal descriptors. After the descriptors were calculated, a probability density function was constructed for each of the descriptors. The program that implemented the proposed recognition method was executed online within an Android application of the smartphone. Signals from two male and two female subjects were considered as a reference for exercise recognition. The exercise recognition accuracy was 94.22% for three performed exercises, and 85.33% for all four considered exercises.
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