prindi

Activity classification for real-time wearable systems : effect of window length, sampling frequency and number of features on classifier performance

autor
Allik, Ardo
Pilt, Kristjan
Karai, Deniss
Fridolin, Ivo
Leier, Mairo
Jervan, Gert
vastutusandmed
Ardo Allik, Kristjan Pilt, Deniss Karai, Ivo Fridolin, Mairo Leier, Gert Jervan
allikas
2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES) : Kuala Lumpur, 4-8 December 2016
ilmumiskoht
[S.l.]
kirjastus/väljaandja
IEEE
ilmumisaasta
2016
leheküljed
p. 460-464 : ill
konverentsi nimetus, aeg
2016 IEEE EMBS Conference on Biomedical Engineering and Sciences, 4-8 December, 2016
konverentsi toimumispaik
Kuala Lumpur, Malaysia
leitav
https://doi.org/10.1109/IECBES.2016.7843493
märksõna
mobiilseadmed
arvutivõrgud
võtmesõna
physical activity classification
sampling frequency
window length
classification features
activity trackers
wearable systems
ISBN
978-1-4673-7791-1
märkused
Bibliogr.: 15 ref
TTÜ struktuuriüksus
biomeditsiinitehnika instituut
arvutitehnika instituut
keel
inglise
Allik, A., Pilt, K., Karai, D., Fridolin, I., Leier, M., Jervan, G. Activity classification for real-time wearable systems : effect of window length, sampling frequency and number of features on classifier performance // 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES) : Kuala Lumpur, 4-8 December 2016. [S.l.] : IEEE, 2016. p. 460-464 : ill. https://doi.org/10.1109/IECBES.2016.7843493