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Brain-Machine Interface (BMI) has emerged as a powerful tool for assisting disabled people and for augmenting human performance. Up so far, no studies have succeeded in the power augmentation for the multi-DOFs robot based on EEG signals, especially for the complex shoulder joint. In this work, we propose an electromyography (EMG) estimation method based on electroencephalography (EEG) signals to...
Brain-Machine Interface (BMI), has been considered as an effective way to help and support the disabled's daily lives and rehabilitation to use their brain activity information instead of their bodies. As an extension, it is also expected to be used for healthy individuals. In our study, we try to estimate the necessary force/torque information for a subject from his/her EEG signals when using an...
We demonstrate the advantages of motor unit (MU) identification from noninvasively recorded high-density surface electromyograms (EMG) of gastrocnemius medialis, gastrocnemius lateralis and soleus muscles for detailed analysis of motor control changes after two weeks of bed rest. Five young (18–28 years) and five older (53–65 years) healthy subjects participated in the study. High-density surface...
EMG based control becomes the core of the pros-theses, orthoses and rehabilitation devices in the recent research. Though the difficulties of using EMG as a control signal due to the complexity nature of this signal, the researchers employed the pattern recognition technique to overcome this problem. The EMG pattern recognition mainly consists of four stages; signal detection and preprocessing feature...
This article describes a methodology to create high-level algorithms, such as artificial neural networks and other pattern recognition techniques to be implemented in embedded systems. A system was developed on a DSP (Digital Signal Processor), for identification and classification of EMG signals; for this purpose the Code Composer Studio software V3.3 and Matlab package were coupled for programming...
The aim of the proposed work is to evaluate, by simulation, the introduction of a data fusion process from a HD-sEMG grid (8×8) to improve the muscle force estimation from sEMG signal. For this purpose, twelve electrode arrangements are combined to dimension reduction technique (PCA or channel averaging) to obtain a monodimensional sEMG signal. After, this signal is used in a sEMG-force relationship...
This paper proposes an integrated approach for the identification of daily hand movements with a view to control prosthetic members. The raw EMG signal is decomposed into Intrinsic Mode Functions (IMFs) with the use of Empirical Mode Decomposition (EMD). A number of features are extracted in time and in frequency domain. Two different dimentionality methods are tested, namely the Principal Component...
Myoelectric signal analysis provides insight into neural control during muscle contraction and it has been widely used to identify the intention of performing different movements for patients with disabilities. Previous studies have demonstrated that detailed neural control information could be extracted from high-density surface electromyography (EMG) signals. However, this imposes practical constraints...
Surface Electromyogram (EMG) signals recorded from an amputee's residual muscles have been investigated as a source of control for prosthetic devices for many years. Despite the extensive research focus on the EMG control of arm and gross hand movements, more dexterous individual and combined prosthetic fingers control has not received the same amount of attention. To facilitate such a control scheme,...
This paper presents a method for providing volitional control of a powered knee prosthesis during nonweight-bearing activity such as sitting. The method utilizes an impedance framework, such that the joint can be programmed with a given stiffness and damping that reflects the nominal impedance properties of an intact joint. Volitional movement of the knee joint is commanded via the stiffness set-point...
Myoelectric or electromyogram (EMG) signals can be useful in intelligently recognizing intended limb motion of a person. This paper presents an attempt to develop a four-channel EMG signal acquisition system as part of an ongoing research in the development of an active prosthetic device. The acquired signals are used for identification and classification of six unique movements of hand and wrist,...
The objective of this work is to study the functional hand strength for ADL rehabilitation tasks based on EMG signals and gripping/pinch force measurement via multivariate data analysis (MVA): Principal Components Analysis (PCA) and Projections to Latent Structures (PLS). The study allows us to identify weak muscles of patients with motor weakness, such as spinal cord injury (SCI) and post-stroke...
We develop a multi-channel electromyogram acquisition system base on the programmable system on chip (PSOC) microcontroller to control robotic arm. The array of 4x4 surface electrodes which invents from the low-cost EKG electrodes is used as the input sensor. B-spline interpolation technique has been utilized to map the EMG signal on the muscle surface. The topological mapping of the EMG is then analyzed...
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