title: Discrimination of forearm's motions by surface EMG signals using neural network.
abstract: We tried to discriminate different forearm's motions by surface EMG signals using neural network. In order to get a higher discrimination rate, the positions of electrodes were improved. We also tried to discriminate similar motions in order to clarify the limitation of the discrimination by surface EMG signals. Two experiments were carried out. One was to discriminate five different motions: grasp, wrist flexion, wrist extension, forearm pronation, and forearm supination (Experiment 1). The other was to discriminate four similar motions which have different quantitative definitions at grasp, wrist flexion/ extension, or forearm pronation/supination (Experiment 2). Four surface electrodes were placed on the skin above the main active muscles: short radial extensor m. of wrist, supinator m., long radial extensor m. of wrist, and ulnar flexor m. of wrist, considering anatomical functions of the forearm's muscles. EMG signals were recorded during 2 sec while the subjects kept the motions. Recorded EMG signals were sampled at 200 msec intervals after full-wave rectifying and low-pass filtering. Therefore, the number of sampling data patterns of EMG signals was 10 for every motion. Three layers of neural network was used for discrimination. The number of units in the input layer is 4, and the number of units in the output layer is 5 or 4. In order to get the best discrimination rate of the motions, we changed the number of units in the hidden layer from 3 to 12. The neural network was trained by the back-propagation algorithm. In Experiment 1, the best average values of discrimination rates under three patterns of EMG signals for each subject were 96.0%, 98.0%, and 87.2% when the numbers of units in the hidden layer were 10, 11, and 3 respectively. In Experiment 2 using original EMG patterns, the best average values of discrimination rates at grasp, extension/flexion, and pronation/supination were 59.5%, 76.0%, and 25.0% respectively. By using normalized EMG patterns, these were 40.0%, 84.8%, and 55.5% respectively.
authors.affiliation: Department of Communications and Systems Engineering, University of Electro-Communications, Tokyo, Japan.
authors.forename: N
authors.initials: N
authors.lastname: Itakura
authors.forename: Y
authors.initials: Y
authors.lastname: Kinbara
authors.forename: T
authors.initials: T
authors.lastname: Fuwa
authors.forename: K
authors.initials: K
authors.lastname: Sakamoto
mesh_terms: D000328:Adult; D000465:Algorithms; D004576:Electromyography; D005542:Forearm / Q000502:physiology*; D018737:Hand Strength / Q000502:physiology; D006801:Humans; D008297:Male; D009038:Motion; D018482:Muscle, Skeletal / Q000502:physiology*; D016571:Neural Networks, Computer; D018583:Pliability; D013997:Time Factors