No abstract

Grasping of the objects is the most frequent activity performed by the human upper limb. The amputations of the upper limb results in the need for prosthetic devices. The myoelectric prosthetic devices use muscle signals and apply control techniques for identification of different levels of hand gesture and force levels. In this study; a different level force contraction experiment was performed in which Electromyography (EMGs) signals and fingertip force signals were acquired. Using this experimental data; a two-step feature selection process is applied for the designing of a pattern recognition algorithm for the classification of different force levels. The two step feature selection process consist of generalized feature ranking using ReliefF, followed by personalized feature selection using Neighborhood Component Analysis (NCA) from the shortlisted features by earlier technique. The classification algorithms applied in this study were Support Vector Machines (SVM) and Random Forest (RF). Besides feature selection; optimization of the number of muscles during classification of force levels was also performed using designed algorithm. Based on this algorithm; the maximum classification accuracy using SVM classifier and two muscle set was achieved as high as 99%. The optimal feature set consisted features such as Auto Regressive coefficients, Willison Amplitude and Slope Sign Change. The mean classification accuracy for different subjects, achieved using SVM and RF was 94.5% and 91.7% respectively.

In this paper, we propose and implement a new lossy method to compress a digital image. We perform technique like quantization, smoothing before applying our novel, simple addition based technique to compress the image. The image is divided into set of 4*4 blocks. The results are good and comparable to JPEG compression algorithm, particularly when image quality is high. The compression ratio in the proposed method is high when adjacent pixel values in the image are close, which is usual in real images. (Abstract)

The hand amputee is deprived of number of activities of daily living. To help the hand amputee, it is important to learn the pattern of muscles activity. There are several elements of tasks, which involve forearm along with the wrist and hand. The one very important task is pick and place activity performed by the hand. A pick and place action is a compilation of different finger motions for the grasping of objects at different force levels. This action may be better understood by learning the electromyography signals of forearm muscles. Electromyography is the technique to acquire electrical muscle activity that is used for the pattern recognition technique of assistive devices. Regarding this, the different classification characterizations of EMG signals involved in the pick and place action, subjected to variable grip span and weights were considered in this study. A low-level force measuring gripper, capable to bear the changes in weights and object spans was designed and developed to simulate the task. The grip span varied from 6 cm to 9 cm and the maximum weight used in this study was 750 gms. The pattern recognition classification methodology was performed for the differentiation of phases of the pick and place activity, grip force, and the angular deviation of metacarpal phalangeal (MCP) joint. The classifiers used in this study were decision tree (DT), support vector machines (SVM) and k-nearest neighbor (k-NN) based on the feature sets of the EMG signals. After analyses, it was found that k-NN performed best to classify different phases of the activity and relative deviation of MCP joint with an average classification accuracy of 82% and 91% respectively. However; the SVM performed best in classification of force with a particular feature set. The findings of the study would be helpful in designing the assistive devices for hand amputee.

In this paper, different machine learning and tabular learning classification algorithms have been studied and compared on the acute hand-gesture Electromyogram dataset. The comparative study between different models such as KNN, RandomForest, TabNet, etc. depicts that small datasets can achieve high-level accuracy along with the intuition of high-performing neural net architectures through tabular learning approaches like TabNet. The performed analysis produced an accuracy of 99.9% through TabNet while other conventional classifiers also gave satisfactory results with KNN being at highest achieving accuracy of 97.8 %.