Simultaneous and Continuous Estimation of Shoulder and Elbow Kinematics from Surface EMG Signals

• Published in: Frontiers in neuroscience Vol. 11; p. 280
• Main Authors: Zhang, Qin, Liu, Runfeng, Chen, Wenbin, Xiong, Caihua
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 30.05.2017; Frontiers Media S.A
• Subjects: Arm; artificial neural network (ANN); Associative learning; Brain research; Elbow; Electromyography; Exoskeleton; independent component analysis (ICA); International conferences; Kinematics; Muscle contraction; myoelectric control; Neural networks; Neuroscience; Physiology; Principal components analysis; principle component analysis (PCA); Prosthetics; Rehabilitation; Robots; Shoulder; simultaneous and continuous motion estimation; artificial neural network (ANN); principle component analysis (PCA); independent component analysis (ICA); myoelectric control; simultaneous and continuous motion estimation
• DOI: 10.3389/fnins.2017.00280
• ISSN: 1662-453X; 1662-4548

In this paper, we present a simultaneous and continuous kinematics estimation method for multiple DoFs across shoulder and elbow joint. Although simultaneous and continuous kinematics estimation from surface electromyography (EMG) is a feasible way to achieve natural and intuitive human-machine interaction, few works investigated multi-DoF estimation across the significant joints of upper limb, shoulder and elbow joints. This paper evaluates the feasibility to estimate 4-DoF kinematics at shoulder and elbow during coordinated arm movements. Considering the potential applications of this method in exoskeleton, prosthetics and other arm rehabilitation techniques, the estimation performance is presented with different muscle activity decomposition and learning strategies. Principle component analysis (PCA) and independent component analysis (ICA) are respectively employed for EMG mode decomposition with artificial neural network (ANN) for learning the electromechanical association. Four joint angles across shoulder and elbow are simultaneously and continuously estimated from EMG in four coordinated arm movements. By using ICA (PCA) and single ANN, the average estimation accuracy 91.12% (90.23%) is obtained in 70-s intra-cross validation and 87.00% (86.30%) is obtained in 2-min inter-cross validation. This result suggests it is feasible and effective to use ICA (PCA) with single ANN for multi-joint kinematics estimation in variant application conditions.