PCA and deep learning based myoelectric grasping control of a prosthetic hand

Abstract

BackgroundFor the functional control of prosthetic hand, it is insufficient to obtain only the motion pattern information. As far as practicality is concerned, the control of the prosthetic hand force is indispensable. The application value of prosthetic hand will be greatly improved if the stable grip of prosthetic hand can be achieved. To address this problem, in this study, a bio-signal control method for grasping control of a prosthetic hand is proposed to improve patient’s sense of using prosthetic hand and the thus improving the quality of life.MethodsA MYO gesture control armband is used to collect the surface electromyographic (sEMG) signals from the upper limb. The overlapping sliding window scheme are applied for data segmentation and the correlated features are extracted from each segmented data. Principal component analysis (PCA) methods are then deployed for dimension reduction. Deep neural network is used to generate sEMG-force regression model for force prediction at different levels. The predicted force values are input to a fuzzy controller for the grasping control of a prosthetic hand. A vibration feedback device is used to feed grasping force value back to patient’s arm to improve patient’s sense of using prosthetic hand and realize accurate grasping. To test the effectiveness of the scheme, 15 able-bodied subjects participated in the experiments.ResultsThe classification results indicated that 8-channel sEMG applying all four time-domain features, with PCA reduction from 32 to 8 dimensions results in the highest classification accuracy. Based on the experimental results from 15 participants, the average recognition rate is over 95%. On the other hand, from the statistical results of standard deviation, the between-subject variations ranges from 3.58 to 1.25%, proving that the robustness and stability of the proposed approach.ConclusionsThe method proposed hereto control grasping power through the patient’s own sEMG signal, which achieves a high recognition rate to improve the success rate of grip and increases the sense of operation and also brings the gospel for upper extremity amputation patients.

Deep Learning in Physiological Signal Data: A Survey

“…Hand motion recognition [9][10][11][12][13][14][15][16][17], Muscle activity recognition [18][19][20][21][22][23] ECG Heartbeat signal classification , Heart disease classification [49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], Sleep-stage classification [64][65][66][67][68], Emotion classification [69], age and gender prediction [70] EEG Brain functionality classification , Brain disease classification , Emotion classification [122][123][124][125][126][127][128][129], Sleep-stage classification [130][131][132][133]…”

“…For the hand-grasping classification described by Li et al [14], principal component analysis (PCA) method is used for dimension reduction and DNN with a stack of 2-layered auto-encoders, and a SoftMax classifier is applied for classifying levels of force.…”

Research on Recognition of Nine Kinds of Fine Gestures Based on Adaptive AdaBoost Algorithm and Multi-Feature Combination

“…At the same time, since it is considered that the amount of data to be processed is large, it is necessary to select a muscle group that is as small as possible and has a substantial correlation. Through the search for a large number of related literatures [20]- [25] and experimental tests, it was found that the three muscle groups have a great correlation with the determined nine fine gestures: the abductor halluces muscle (APL), the superficial flexor (FDS), ulnar wrist flexor (FCU). After determining the location of the muscle group, place the three Music Muscle sensors in these three locations.…”

Pattern Recognition in Myoelectric Signals Using Deep Learning, Features Engineering, and a Graphics Processing Unit

“…One of the branches is myography, which studies the electrical signal from the brain, whose responsibility is muscle control. The myoelectric wave (EMG) provides a load of information that is employed to perform a physical diagnosis and develop systems that permit the conception of intelligent prostheses [6]. This research addresses the problem of classifying movements according to the EMG.…”