The Fusion of HRV and EMG Signals for Automatic Gender Recognition during Stepping Exercise

• Published in: Telkomnika Vol. 15; no. 2; p. 756
• Main Authors: Mohd Rosli, Nor Aziyatul Izni, Abdul Rahman, Mohd Azizi, Balakrishnan, Malarvili, Amri Mazlan, Saiful, Zamzuri, Hairi
• Format: Journal Article
• Language: English
• Published: Yogyakarta Ahmad Dahlan University 01.06.2017
• Subjects: Algorithms; Classification; Communication; Computer engineering; Electromyography; Feature extraction; Forensic sciences; Gender differences; Heart rate; Identification; International conferences; Physiology; R&D; Redundancy; Rehabilitation; Research & development; Seizures; Short term; Signal classification; Signal processing; Software
• ISSN: 1693-6930; 2302-9293
• DOI: 10.12928/telkomnika.v15i2.6113

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 a stepper machine is proposed. Furthermore, there are many different techniques for solving the problem of gender recognition such as based on fusion of face and gait information [7], fusion of facial strips [8], color information [9], sift features [10], fusion of different spatial scale features elected by mutual information from the hyhistogram of LBP (local binary patterns), intensity, and shape [11]. Next, there are methods based on extraction of the hip joint data that was computed from the Biovision Hierarchical data [14] and from gait sequences with arbitrary walking directions [15]. [...]it is true that physiological signals are also able to be implemented in gender classification. [...]the physiological signals which are EMG and HRV signals collected during stepping activity are utilized in this research. [...]the information from both EMG and HRV is combined together to get better classification result. Kubios HRV is progressive and convenient to utilize the software for HRV analysis. [...]the software supports a few input information for ECG data and RR interval (RRi) data. First International Conference on Robot, Vision and Signal Processing, Kaohsiung. 2011: 40-43. Heart Rate Variability: Standards of Measurement, physiological Interpretation, and Clinical Use. [21] Malarvili MB, Mesbah M, Boashash B. HRV Feature Selection Based on Discriminant and Redundancy Analysis for Neonatal Seizure Detection. 6th International Conference on Information, Communications and Signal Processing, Singapore...