Feature and Channel Selection Using Correlation Based Method for Hand Posture Classification in Multiple Arm Positions

In this work we propose a method based on correlation-based feature selection (CFS) to select features and channels for pattern recognition control of upper-limb prostheses. This method was applied on features extracted from myoelectric signals acquired from two able-bodied subjects and one individual with transradial amputation while contracting the muscles as to perform five functional hand postures in nine arm positions. The classification accuracy increased by using CFS for the able-bodied, while no statistical improvements has been highlighted for the amputee subject. The channels selected by this approach were mainly placed on the posterior side of the forearm which might reflect importance role of the extensor muscles over the flexor muscle when performing these hand postures. Further analysis with bigger dataset will be conducted to validate these preliminary findings.