Human Computer Interaction / HCI
Hadi Soltanizadeh; Pouria Sharifi; Ali Maleki
Volume 16, Issue 3 , December 2022, , Pages 241-255
Abstract
Losing of voice and larynx is a major problem for people with speech disorders. It creates serious and negative consequences on the quality of individual and group life of these people, especially in working environments. The development of an intelligent system based on electromyogram signals with the ...
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Losing of voice and larynx is a major problem for people with speech disorders. It creates serious and negative consequences on the quality of individual and group life of these people, especially in working environments. The development of an intelligent system based on electromyogram signals with the ability to recognize speech (without using sound) can be a window of hope for people who lost their larynx and voice due to cancer. Although progress and studies in this field are growing in our country and in different languages, but these studies have not been done for the Persian language. In this article, for the first time, recognition of Persian words was done using electromyogram of facial muscles. For this purpose, sEMG signals were collected from eight facial muscles and six volunteers while speaking twelve Persian words. Then, MFL, VAR, DAMV, LTKE, IQR and Cardinality features were extracted from each channel and each window from the signal, and the 432 features from each signal were reduced to 33 features using the PCA principal component analysis method. Finally, in order to recognize twelve Persian words, the features were given to SVM, KNN and RF classifiers. The average classification accuracy was 83.16%, 81.91% and 78.97%, respectively. Our evaluation in this article gives the hope that by using EMG signals it is possible to recognize the limited words of Persian language.
Seyedeh Somayeh Naghibi; Ali Fallah; Ali Maleki; Farnaz Ghassemi
Volume 13, Issue 3 , October 2019, , Pages 247-257
Abstract
The correct prediction of the optimal motor trajectory is necessary for movement rehabilitation and control systems such as functional electrical stimulation and robotic therapy. It seems that human reaching movements are composed of a set of submovements, each of which is a correction of the overall ...
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The correct prediction of the optimal motor trajectory is necessary for movement rehabilitation and control systems such as functional electrical stimulation and robotic therapy. It seems that human reaching movements are composed of a set of submovements, each of which is a correction of the overall movement trajectory. Therefore, it is possible to interpret complex movements, learning, adaptability and other features of the motion control system using submovements. The purpose of this study is predicting and generating planar reaching movements using a realistic model similar to the actual mechanism of human movement and based on the submovement. The data used consists of different replications of four types of planar movement Performed by three healthy subjects. After the preprocessing and phasing, the movements decomposed to minimum-jerk submovement. In the next step, the training of three distinct neural networks was carried out to learn the submovement parameters including the amplitude, duration, and initiation time. Finally, the ANNs were combined to form a closed-loop model that generated accurate reaching movements based on the error correction. The target access rate for all predicted movements by the closed loop model was 100%. Also, the mean distance to the target, the VAF, and the mean MSE error between the predicted and main movement trajectory showed that the predicted movements are a good approximation of the main movements. The results showed that when trained neural networks with submovements, were placed in a closed loop model, they were able to predict proper submovements for complete access to targets due to the compensation of propagated errors from the previous steps. The results of this study can be used to improve motor rehabilitation methods.
Neuro-Muscular Engineering
Sahar Babaei; Ali Maleki
Volume 8, Issue 1 , March 2014, , Pages 57-68
Abstract
Nowadays real time motion tracking have been receiving considerable attention in many applications and research fields such as rehabilitation, medicine and treatment. Recently MEMS accelerometers play an important role to attend desired result for these applications. This paper presents a new design ...
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Nowadays real time motion tracking have been receiving considerable attention in many applications and research fields such as rehabilitation, medicine and treatment. Recently MEMS accelerometers play an important role to attend desired result for these applications. This paper presents a new design for angle measurement device based on accelerometer sensor and Bluetooth module. Using Bluetooth module in addition to providing minimally obtrusive recording, allows you to connect to your personal computer and mobile quicker and easier. This system has made up of 2 complete 3 axis accelerometer ADXL330, which by giving sufficient data in 3D space allows us to investigate joint angle with DCMR method. The mentioned method in dynamic recording remarkably has less error in comparison to CMR method. As one application for this system, determination of elbow joint angle is studied. Eventually experimental recording of elbow joint angle in static and dynamic condition was done by applying CMR method. With reference to electrogoniometer output the maximum static and dynamic error were obtained respectively 3 and 6.1 degrees.
Neuro-Muscular Engineering
Sohrab Barimani; Ali Maleki; Ali Fallah
Volume 8, Issue 1 , March 2014, , Pages 101-111
Abstract
FES based method used for rehabilitation of patients with spinal cord injury (SCI). One of these methods is FES cycling. FES cycling exercise has to be useful among SCI patients because of creating a periodic activity in the muscles of the lower extremities and stability of seating position. The major ...
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FES based method used for rehabilitation of patients with spinal cord injury (SCI). One of these methods is FES cycling. FES cycling exercise has to be useful among SCI patients because of creating a periodic activity in the muscles of the lower extremities and stability of seating position. The major challenge for application of FES in rehabilitation is early fatigue occurrence in electrically stimulated muscles. Motor control system selects a low-cost path among the infinite possible route to the body's movements. High efficiency and the minimum rate of muscle fatigue are main characteristics of the motor control system. This type of control system is called muscle synergy. In this study, the quantification of muscle synergy between the core muscles in cycling has been done by non-negative matrix factorization (NMF) method and considering the kinesiology basis. Four synergies were determined as appropriate and optimal synergies to describe the cycling in different mechanical terms. VAF criteria with regard to the four synergies to describe cycling in speeds of 40, 50 and 60 rpm are 92±4, 92±3 and 91±4% respectively and torques, 5, 7 and 9 Nm are 91±3, 92±5 and 92±4% respectively. Correlation between Synergies extracted at different mechanical terms is 98.4 percent in average.
Rehabilitation Engineering
Ali Maleki; Ali Fallah
Volume 2, Issue 2 , June 2008, , Pages 131-140
Abstract
Patients with spinal cord injury in C5/C6 levels are capable of controlling the voluntary movements of the shoulder joints, but some muscles involved in the movement of the elbow joint are paralyzed in these patients. By using FES as well as an appropriate stimulation of the paralyzed muscles, the patients ...
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Patients with spinal cord injury in C5/C6 levels are capable of controlling the voluntary movements of the shoulder joints, but some muscles involved in the movement of the elbow joint are paralyzed in these patients. By using FES as well as an appropriate stimulation of the paralyzed muscles, the patients can be assisted with their essential daily living activities. One of the major problems of using FES for reanimation of the paralyzed arm is to provide voluntary commands for FES control. Kinematic synergy and muscle synergy are two main options in this regard. In this paper, these two command sources were evaluated and compared. Furthermore, a mixed method was proposed, which improves performance. Thus, the EMG and kinematical data during a set of activities of daily living (AOL) were recorded and processed. Precise investigations were carried out in order to determine the appropriate values for high-level neural network controller parameters. Next, six different neural network controller structures were trained by the EMG and/or kinematical data. Using this method, cross correlation between the estimation and measurement for all records was obtained as 94.76% for kinematic synergy and 98.08%, for muscle synergy. In the mixed method, these values were improved to 94.82% and 98.84% respectively. Furthermore, mixed method paved the way to improve the performance of low-level controller with estimating the desired kinematics for the distal joint and desired activity for the paralyzed muscle.
