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Gesture recognition of wrist motion using low-frequency PPG
RYLO, MARCOS NEGREIROS; SILVA, WALMIR A.; PAIVA DE MEDEIROS, RENAN LANDAU; DE LUCENA JUNIOR, VICENTE FERREIRA
Resumo
Resumo
This paper evaluated two machine learning techniques using low-frequency photoplethysmography and motion sensor data from wearable devices in gesture segmentation and classification. SVM and random forests were the classifiers selected for testing. Preliminary evaluations show that frequencies of 25 Hz are suitable for the recognition process, achieving an F1-score of 0.819 for seven gesture sets.
Autoria
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palavras-chave
Palavras-chave
gesture recognitionwrist kinematicswearable sensorsgesture segmentationgesture classificationsupport vector machinerandom forest
Status
Published
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Trabalho em anais de evento