The Fusion of HRV and EMG Signals for Automatic Gender Recognition during Stepping Exercise
Abstract: In this paper, a new
gender recognition framework based on fusion of features extracted from healthy
people electromyogram (EMG) and heart rate variability (HRV) during stepping
activity using astepper machine is proposed. An approach is investigated for
the fusion of EMG and HRV which is feature fusion. The feature fusion is
carried out by concatenating the feature vector extracted from the EMG and HRV
signals. A proposed framework consists of a sequence of processing steps which
are preprocessing, feature extraction, feature selection and lastly the fusion.
The results shown that the fusion approach had improved the performance of
gender recognition compared to solely on EMG or HRV based gender identifier.
Keywords: gender recognition,
feature fusion, heart rate variability (HRV), electromyography (EMG), Sstepper
Author: Nor Aziyatul Izni Mohd
Rosli
JournaL Code: jptkomputergg170090
