User daily activity classification from accelerometry using feature selection and SVM

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5517 LNCS, Issue PART 1, 2009, Pages 1137-1144

  Parera, Jordi ^a   Angulo, Cecilio ^a   Rodriguez Molinero A ^b   Cabestany, Joan ^a  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

^b Sant Antoni Abad Hospital   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ACCELEROMETRY; ACTIVITIES RECOGNITION; DAILY ACTIVITY; FEATURE SELECTION; FEATURES EXTRACTION; PROCESSING NODES; RAW SIGNALS; REAL TIME; REAL TIME MONITORING; TRIAXIAL ACCELEROMETER;

ACCELEROMETERS; BACKPROPAGATION; FEATURE EXTRACTION; SUPPORT VECTOR MACHINES;

NEURAL NETWORKS;

EID: 68749108637     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02478-8_142     Document Type: Conference Paper

References (5)

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3. Bouten, C.V.C., Koekkoek, K.T.M., Verduin, M., Kodde, R., Janssen, J.D.: A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE Transactions on Biomedical Engineering 44(3), 136-147 (1997)
4. Begg, R.K., Palaniswami, M., Owen, B.: Support vector machines for automated gait classification. IEEE Transactions on Biomédical Engineering 52(5), 828-838 (2005)
5. Isabelle, G., André, E.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157-1182 (2003)