TY - GEN
T1 - Low-power activity recognition from triaxial accelerometer data
AU - Chiossi, Maximiliano
AU - Miguez, Matias
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - In this work, a low power triaxial accelerometer data acquisition prototype is presented, which is used to derive physical activity recognition algorithms to be used for implantable or wearable applications. There different characterization methods were implemented and can predict the different activities with good accuracy (380 cases out of 386).
AB - In this work, a low power triaxial accelerometer data acquisition prototype is presented, which is used to derive physical activity recognition algorithms to be used for implantable or wearable applications. There different characterization methods were implemented and can predict the different activities with good accuracy (380 cases out of 386).
KW - Low power
KW - accelerometer
KW - activity recognition
UR - http://www.scopus.com/inward/record.url?scp=85084038063&partnerID=8YFLogxK
U2 - 10.1109/PRIME-LA47693.2020.9062703
DO - 10.1109/PRIME-LA47693.2020.9062703
M3 - Contribución a la conferencia
AN - SCOPUS:85084038063
T3 - PRIME-LA 2020 - 3rd IEEE Conference on Ph.D. Research in Microelectronics and Electronics in Latin America, Proceedings
BT - PRIME-LA 2020 - 3rd IEEE Conference on Ph.D. Research in Microelectronics and Electronics in Latin America, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE Conference on Ph.D. Research in Microelectronics and Electronics in Latin America, PRIME-LA 2020
Y2 - 25 February 2020 through 28 February 2020
ER -