A framework to detect and classify activity transitions in low-power applications

Proceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009

Volumn , Issue , 2009, Pages 1716-1719

  Boyd, Jeffrey ^a   Sundaram, Hari ^a  

^a ARIZONA STATE UNIVERSITY   (United States)

Author keywords

Gesture recognition; HMM; Inertial sensors; Low power; Wavelet analysis

Indexed keywords

ACCELEROMETER DATA; ACTIVITIES OF DAILY LIVING; BATTERY LIFE; FEATURE SETS; HMM; INERTIAL SENSORS; LOG-LIKELIHOOD RATIO TEST; LOW POWER; LOW POWER APPLICATION; LOW-POWER DEVICES;

ELECTRIC EQUIPMENT; GESTURE RECOGNITION; INERTIAL NAVIGATION SYSTEMS; MAXIMUM LIKELIHOOD ESTIMATION; MULTIMEDIA SYSTEMS; SMART SENSORS; SPEECH RECOGNITION; WAVELET ANALYSIS; WAVELET TRANSFORMS;

HIDDEN MARKOV MODELS;

EID: 70449574501     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICME.2009.5202851     Document Type: Conference Paper

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