Pregled bibliografske jedinice broj: 595796
Accelerometer Based Gesture Recognition System Using Distance Metric Learning for Nearest Neighbour Classification
Accelerometer Based Gesture Recognition System Using Distance Metric Learning for Nearest Neighbour Classification // Proc. 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2012) / Santamaría, Ignacio ; Arenas-García, Jerónimo ; Camps-Valls, Gustavo ; Erdogmus, Deniz ; Pérez-Cruz, Fernando ; Larsen, Jan (ur.).
Santander, Španjolska, 2012. (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 595796 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Accelerometer Based Gesture Recognition System Using Distance Metric Learning for Nearest Neighbour Classification
Autori
Marasović, Tea ; Papić, Vladan
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proc. 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2012)
/ Santamaría, Ignacio ; Arenas-García, Jerónimo ; Camps-Valls, Gustavo ; Erdogmus, Deniz ; Pérez-Cruz, Fernando ; Larsen, Jan - , 2012
ISBN
978-1-4673-1025-3
Skup
2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2012)
Mjesto i datum
Santander, Španjolska, 23.09.2012. - 26.09.2012
Vrsta sudjelovanja
Poster
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
accelerometer- based gesture recognition; distance metric learning; NN classification; convex optimization
(accelerometer-based gesture recognition; distance metric learning; NN classification; convex optimization)
Sažetak
The need to improve communication between humans and computers has been motivation for defining new communication models, and accordingly, new ways of interacting with machines. In many applications today, user interaction is moving away from traditional keyboards and mouses and is becoming much more physical, pervasive and intuitive. This paper examines hand gestures as an alternative or supplementary input modality for mobile devices. A new gesture recognition system based on the use of acceleration sensor, that is nowadays being featured in a growing number of consumer electronic devices, is presented. Accelerometer sensor readings can be used for detection of hand movements and their classification into previously trained gestures. The proposed system utilizes Mahalanobis distance metric learning to improve the accuracy of nearest neighbour classification. In the approach we adopted, the objective function for metric learning is convex and, therefore, the required optimization can be cast as an instance of semidefinite programming. The experiments, carried out to evaluate system performance, demonstrate its efficacy.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo
POVEZANOST RADA
Projekti:
023-0232006-1655 - Biomehanika ljudskih pokreta, upravljanje i rehabilitacija (Zanchi, Vlasta, MZOS ) ( CroRIS)
023-0232006-1662 - Računalni vid u identifikaciji kinematike sportskih aktivnosti (Papić, Vladan, MZOS ) ( CroRIS)
177-0232006-1662 - Računalni vid u identifikaciji kinematike sportskih aktivnosti
Ustanove:
Fakultet elektrotehnike, strojarstva i brodogradnje, Split,
Prirodoslovno-matematički fakultet, Split