Handwritten Digits Recognition From sEMG: Electrodes Location and Feature Selection

نویسندگان

چکیده

Despite hand gesture recognition is a widely investigated field, the design of myoelectric architectures for detecting finer motor task, like handwriting, less studied. However, writing tasks involving cognitive loads represent an important aspect toward generalization myoelectric-based human-machine interfaces (HMI), and also many rehabilitative tasks. In this study, handwriting ten digits was faced under control perspective, considering probes setup feature extraction step. Time frequency domain features were extracted from surface electromyography (sEMG) signals 11 subjects who wrote following standardized template 8 sEMG equally distributed between forearm wrist. Feature class separability aggregated set built to train pattern architectures, i.e. linear discriminant analysis (LDA) quadratic support vector machine (QSVM). Also, four reduced setups investigated. LDA QSVM showed mean accuracy about 97%, with all wrist information. A significant reduction performances observed or only ( $\leq 92$ %) when trained two electrodes information 90$ %). For reliable classification in task high demands, it required use fully covering Outcomes methodological transfer recognition, which represents key development new HMI rehabilitation

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Handwritten digits recognition using OpenCV

The automated recognition of handwritten digits is a largely studied problem which connects the fields of Computer Vision and Machine Learning and has many applications in real life. In this project, I detail an introductory investigation of the performance of classification in several contexts. Namely, relying on the OpenCV implementations of k-Nearest Neighbor, Random Forests, and Support Vec...

متن کامل

Recognition of Handwritten Digits Using Deformable Models

Deformable models are used to recognize handwritten characters which have a great variety of handwriting styles. The overall character shape is modeled by a B-spline and individual pixels are modeled by Gaussian functions. Model parameters associated with the spline and the Gaussian functions, together with their relative strength, are estimated using Bayesian inference. Under such a Bayesian f...

متن کامل

recognition of handwritten farsi digits by shape matching

in this paper, we used a shape matching algorithm to recognize farsi digits. for each sampled point on the contour of a shape, we obtain a descriptor showing the distribution of the other points of the contour, with respect to this point. based on these descriptors, we find the corresponding points of the two contours and take the sum of their distances as a dissimilarity measure between two sh...

متن کامل

Recognition of Handwritten Digits Using Multilayer Perceptrons

Neural networks are often used for pattern recognition. They prove to be a popular choice for OCR (Optical Character Recognition) systems, especially when dealing with the recognition of printed text. In this paper, multilayer perceptrons are used for the recognition of handwritten digits. The accuracy achieved proves that this application is a working prototype that can be further extended int...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: IEEE Access

سال: 2023

ISSN: ['2169-3536']

DOI: https://doi.org/10.1109/access.2023.3279735