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1
NAMAZI, HAMIDREZA. "FRACTAL-BASED CLASSIFICATION OF ELECTROMYOGRAPHY (EMG) SIGNAL IN RESPONSE TO BASIC MOVEMENTS OF THE FINGERS." Fractals 27, no. 03 (May 2019): 1950037. http://dx.doi.org/10.1142/s0218348x19500373.
Повний текст джерелаАнотація:
Investigating human movements is the most important issue in rehabilitation science. Movements of fingers as one of the major movements of human has been considered by many scientists. Therefore, decoding of finger movements by analysis of related biosignal is very important to consider. In this research, we do the complexity analysis on the Electromyography (EMG) signal that was recorded due to basic movements of fingers. In fact, the EMG signal was classified in case of different movements of fingers by fractal analysis. The result of analysis showed that the EMG signal has the greatest and lowest fractal dimension (complexity) in case of thumb finger flexion and little finger extension. In further attempts, the fractal theory can be applied to investigate the influence of other types of stimulation on variations of the complexity of muscles’ reactions.
2
Reilly, Karen T., and Marc H. Schieber. "Incomplete Functional Subdivision of the Human Multitendoned Finger Muscle Flexor Digitorum Profundus: An Electromyographic Study." Journal of Neurophysiology 90, no. 4 (October 2003): 2560–70. http://dx.doi.org/10.1152/jn.00287.2003.
Повний текст джерелаАнотація:
The human flexor digitorum profundus (FDP) sends tendons to all 4 fingers. One might assume that this multitendoned muscle consists of 4 discrete neuromuscular compartments each acting on a different finger, but recent anatomical and physiological studies raise the possibility that the human FDP is incompletely subdivided. To investigate the functional organization of the human FDP, we recorded electromyographic (EMG) activity by bipolar fine-wire electrodes simultaneously from 2 or 4 separate intramuscular sites as normal human subjects performed isometric, individuated flexion, and extension of each left-hand digit. Some recordings showed EMG activity during flexion of only one of the 4 fingers, indicating that the human FDP has highly selective core regions that act on single fingers. The majority of recordings, however, showed a large amount of EMG activity during flexion of one finger and lower levels of EMG activity during flexion of an adjacent finger. This lesser EMG activity during flexion of adjacent fingers was unlikely to have resulted from recording motor units in neighboring neuromuscular compartments, and instead suggests incomplete functional subdivision of the human FDP. In addition to the greatest agonist EMG activity during flexion of a given finger, most recordings also showed EMG activity during extension of adjacent fingers, apparently serving to stabilize the given finger against unwanted extension. Paradoxically, the functional organization of the human FDP—with both incomplete functional subdivision and highly selective core regions—may contribute simultaneously to the inability of humans to produce completely independent finger movements, and to the greater ability of humans (compared with macaques) to individuate finger movements.
3
Goen, Anjana, and D. C. Tiwari. "Pattern Recognition of Individual and Combined Fingers Movements Based Prosthesis Control Using Surface EMG Signals." International Journal of Electrical and Electronics Research 3, no. 4 (December 30, 2015): 70–78. http://dx.doi.org/10.37391/ijeer.030401.
Повний текст джерелаАнотація:
Prosthesis control system is the need for the amputees or disable person for performing their daily household work and interaction with the outside world. It is the fundamental component of modern prostheses, which uses the myoelectric signals from an individual’s muscles to control the prosthesis movements. The surface electromyogram signals (SEMG) being noninvasive has been used as a control source for multifunction powered prostheses controllers. In spite of the fact there is wide research on the myoelectric control of movements of forearm and hand movements but a little research has been carried out for control of more dexterous individual and combined fingers. With the current demands of such prostheses a challenge that exists is the ability to precisely control a large number of individual and combined finger movements and that too in a computationally efficient manner. This paper investigates accurate and correct discrimination between individual and combined fingers movements using surface myoelectric signals, in order to control the different finger postures of a prosthetic hand. We have SEMG datasets with eight electrodes located on the human forearm and fifteen classes. Various feature sets are extracted and projected in a manner to ensure that maximum separation exists between the finger movements and then fed to the four different classifiers. Practical results along with the statistical significance tests proved the feasibility of the proposed approach with mean classification accuracy greater than 95% in finger movement classification.
4
Millar, Christopher, Nazmul Siddique, and Emmett Kerr. "LSTM Network Classification of Dexterous Individual Finger Movements." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 2 (March 20, 2022): 113–24. http://dx.doi.org/10.20965/jaciii.2022.p0113.
Повний текст джерелаАнотація:
Electrical activity is generated in the forearm muscles during muscular contractions that control dexterous movements of a human finger and thumb. Using this electrical activity as an input to train a neural network for the purposes of classifying finger movements is not straightforward. Low cost wearable sensors i.e., a Myo Gesture control armband (www.bynorth.com), generally have a lower sampling rate when compared with medical grade EMG detection systems e.g., 200 Hz vs 2000 Hz. Using sensors such as the Myo coupled with the lower amplitude generated by individual finger movements makes it difficult to achieve high classification accuracy. Low sampling rate makes it challenging to distinguish between large quantities of subtle finger movements when using a single network. This research uses two networks which enables for the reduction in the number of movements in each network that are being classified; in turn improving the classification. This is achieved by developing and training LSTM networks that focus on the extension and flexion signals of the fingers and a separate network that is trained using thumb movement signal data. By following this method, this research have increased classification of the individual finger movements to between 90 and 100%.
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Sander, Tilmann H., Stefanie Leistner, Heidrun Wabnitz, Bruno-Marcel Mackert, Rainer Macdonald, and Lutz Trahms. "Cross-Correlation of Motor Activity Signals from dc-Magnetoencephalography, Near-Infrared Spectroscopy, and Electromyography." Computational Intelligence and Neuroscience 2010 (2010): 1–8. http://dx.doi.org/10.1155/2010/785279.
Повний текст джерелаАнотація:
Neuronal and vascular responses due to finger movements were synchronously measured using dc-magnetoencephalography (dcMEG) and time-resolved near-infrared spectroscopy (trNIRS). The finger movements were monitored with electromyography (EMG). Cortical responses related to the finger movement sequence were extracted by independent component analysis from both the dcMEG and the trNIRS data. The temporal relations between EMG rate, dcMEG, and trNIRS responses were assessed pairwise using the cross-correlation function (CCF), which does not require epoch averaging. A positive lag on a scale of seconds was found for the maximum of the CCF between dcMEG and trNIRS. A zero lag is observed for the CCF between dcMEG and EMG. Additionally this CCF exhibits oscillations at the frequency of individual finger movements. These findings show that the dcMEG with a bandwidth up to 8 Hz records both slow and faster neuronal responses, whereas the vascular response is confirmed to change on a scale of seconds.
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Srimaneepong, Viritpon, Artak Heboyan, Azeem Ul Yaqin Syed, Hai Anh Trinh, Pokpong Amornvit, and Dinesh Rokaya. "Recent Advances in Myoelectric Control for Finger Prostheses for Multiple Finger Loss." Applied Sciences 11, no. 10 (May 14, 2021): 4464. http://dx.doi.org/10.3390/app11104464.
Повний текст джерелаАнотація:
The loss of one or multiple fingers can lead to psychological problems as well as functional impairment. Various options exist for replacement and restoration after hand or finger loss. Prosthetic hand or finger prostheses improve esthetic outcomes and the quality of life for patients. Myoelectrically controlled hand prostheses have been used to attempt to produce different movements. The available articles (original research articles and review articles) on myoelectrically controlled finger/hand prostheses from January 1922 to February 2021 in English were reviewed using MEDLINE/PubMed, Web of Science, and ScienceDirect resources. The articles were searched using the keywords “finger/hand loss”, “finger prosthesis”, “myoelectric control”, and “prostheses” and relevant articles were selected. Myoelectric or electromyography (EMG) signals are read by myoelectrodes and the signals are amplified, from which the muscle’s naturally generated electricity can be measured. The control of the myoelectric (prosthetic) hands or fingers is important for artificial hand or finger movement; however, the precise control of prosthetic hands or fingers remains a problem. Rehabilitation after multiple finger loss is challenging. Implants in finger prostheses after multiple finger loss offer better finger prosthesis retention. This article presents an overview of myoelectric control regarding finger prosthesis for patients with finger implants following multiple finger loss.
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Pamungkas, Daniel Sutopo, Sumantri K. Risandriya, and Adam Rahman. "Classification of Finger Movements Using EMG Signals with PSO SVM Algorithm." International Journal of Advanced Science Computing and Engineering 4, no. 3 (December 27, 2022): 210–19. http://dx.doi.org/10.30630/ijasce.4.3.100.
Повний текст джерелаАнотація:
Electromyography (EMG) is a signal produced by human muscles when they contract or relax. This signal is widely used as a controller, for example, to control a robotic arm. This study aims to identify the pattern of finger movement in the form of finger movement using a bracelet-shaped device that has eight EMG sensors. This tool is placed on the lower right hand of a subject to get a signal from the EMG. This study uses the support vector machine (SVM) algorithm combined with the particle swarm optimization (PSO) method. For pattern recognition, the properties of the signal in the time domain are used. From this system, the success of pattern recognition is between 68% to 86%.
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Hore, J., B. Wild, and H. C. Diener. "Cerebellar dysmetria at the elbow, wrist, and fingers." Journal of Neurophysiology 65, no. 3 (March 1, 1991): 563–71. http://dx.doi.org/10.1152/jn.1991.65.3.563.
Повний текст джерелаАнотація:
1. The objective was to investigate in cerebellar patients with dysmetria the kinematic and electromyographic (EMG) characteristics of large and small movements at the elbow, wrist, and finger and thereby to determine the nature of cerebellar dysmetria at distal as well as proximal joints. Flexions were made as fast as possible by moving relatively heavy manipulanda for each joint to the same end position through 5, 30, and 60 degrees. 2. In normal subjects flexions at all joints were accompanied by similar triphasic EMG activity. Movements of increasing amplitude were made with increasing movement durations and increasing durations and magnitudes of initial agonist EMG activity. Antagonist activity often appeared to have two components: one coactive with the initial agonist burst but starting later, the other reaching its peak at about peak velocity. 3. Cerebellar patients with dysmetria showed hypermetria followed by tremor at all three joints when movements were made with the manipulanda. Hypermetria was most marked for aimed movements of small amplitude (5 degrees) at all joints. 4. A characteristic of cerebellar disordered movements, which could be present at all amplitudes and all joints, was an asymmetry with decreased peak accelerations and increased peak decelerations compared to normal movements. Both the asymmetry and the hypermetria for small amplitude movements could be used clinically as sensitive indicators of cerebellar dysfunction. 5. The EMG abnormalities accompanying hypermetria and asymmetry were a more gradual buildup and a prolongation of agonist activity and delayed onset of antagonist activity.(ABSTRACT TRUNCATED AT 250 WORDS)
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Dai, Chenyun, and Xiaogang Hu. "Extracting and Classifying Spatial Muscle Activation Patterns in Forearm Flexor Muscles Using High-Density Electromyogram Recordings." International Journal of Neural Systems 29, no. 01 (January 10, 2019): 1850025. http://dx.doi.org/10.1142/s0129065718500259.
Повний текст джерелаАнотація:
The human hand is capable of producing versatile yet precise movements largely owing to the complex neuromuscular systems that control our finger movement. This study seeks to quantify the spatial activation patterns of the forearm flexor muscles during individualized finger flexions. High-density (HD) surface electromyogram (sEMG) signals of forearm flexor muscles were obtained, and individual motor units were decomposed from the sEMG. Both macro-level spatial patterns of EMG activity and micro-level motor unit distributions were used to systematically characterize the forearm flexor activation patterns. Different features capturing the spatial patterns were extracted, and the unique patterns of forearm flexor activation were then quantified using pattern recognition approaches. We found that the forearm flexor spatial activation during the ring finger flexion was mostly distinct from other fingers, whereas the activation patterns of the middle finger were least distinguishable. However, all the different activation patterns can still be classified in high accuracy (94–100%) using pattern recognition. Our findings indicate that the partial overlapping of neural activation can limit accurate identification of specific finger movement based on limited recordings and sEMG features, and that HD sEMG recordings capturing detailed spatial activation patterns at both macro- and micro-levels are needed.
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Saikia, Angana, Nayan M. Kakoty, Nabasmita Phukan, Malarvili Balakrishnan, Nitin Sahai, Sudip Paul, and Dinesh Bhatia. "Combination of EMG Features and Stability Index for Finger Movements Recognition." Procedia Computer Science 133 (2018): 92–98. http://dx.doi.org/10.1016/j.procs.2018.07.012.
Повний текст джерела
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Phukan, Nabasmita, Nayan M. Kakoty, Prastuti Shivam, and John Q. Gan. "Finger movements recognition using minimally redundant features of wavelet denoised EMG." Health and Technology 9, no. 4 (May 17, 2019): 579–93. http://dx.doi.org/10.1007/s12553-019-00338-z.
Повний текст джерела
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Darling, W. G., and K. J. Cole. "Muscle activation patterns and kinetics of human index finger movements." Journal of Neurophysiology 63, no. 5 (May 1, 1990): 1098–108. http://dx.doi.org/10.1152/jn.1990.63.5.1098.
Повний текст джерелаАнотація:
1. The present study was conducted to determine whether dynamic interaction torques are significant for control of digit movements and to investigate whether such torques are compensated by specific muscle activation patterns. 2. Angular positions of the metacarpophalangeal (MP) and proximal interphalangeal (PIP) joints of the index finger in the flexion/extension plane were recorded with the use of planar electrogoniometers. Muscle activation patterns were monitored with the use of fine wire and surface electromyography of intrinsic and extrinsic finger muscles. 3. Dynamic interaction torques associated with index finger movements were large in relation to joint torques produced by muscles, especially in faster movements. The significance of dynamic interaction torques was demonstrated in model simulations of two-joint finger motion in response to joint torque inputs. Removal of interaction torques from the model inputs produced movements that differed greatly from digit motions produced by human subjects. 4. Electromyogram (EMG) and torque patterns associated with finger movements of different speeds indicated that muscle activity is necessary not only for producing motion at the joints but also to counteract segmental interaction torques. This was especially evident during movements that required voluntary maintenance of a constant MP joint angle during motion of the distal segment about the PIP joint. Under these conditions, muscle moments acting at the MP acted directly to counteract torques at the MP arising from motion at the PIP. 5. Neural mechanisms underlying control of index finger movement are discussed with reference to the implications of dynamic interaction torques. Potential control strategies include accurate programming of muscle activation patterns, appropriate use of motion-dependent peripheral afferent information, and control of the finger as a viscoelastic system through coactivation of flexor and extensor musculature. It is concluded that additional research incorporating study of motion in three dimensions and the use of mechanical models of the finger and related musculature is required to determine how interaction torques are compensated during finger motion.
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Arteaga, Maria V., Jenny C. Castiblanco, Ivan F. Mondragon, Julian D. Colorado, and Catalina Alvarado-Rojas. "EMG-driven hand model based on the classification of individual finger movements." Biomedical Signal Processing and Control 58 (April 2020): 101834. http://dx.doi.org/10.1016/j.bspc.2019.101834.
Повний текст джерела
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Winges, Sara A., Shinichi Furuya, Nathaniel J. Faber, and Martha Flanders. "Patterns of muscle activity for digital coarticulation." Journal of Neurophysiology 110, no. 1 (July 1, 2013): 230–42. http://dx.doi.org/10.1152/jn.00973.2012.
Повний текст джерелаАнотація:
Although piano playing is a highly skilled task, basic features of motor pattern generation may be shared across tasks involving fine movements, such as handling coins, fingering food, or using a touch screen. The scripted and sequential nature of piano playing offered the opportunity to quantify the neuromuscular basis of coarticulation, i.e., the manner in which the muscle activation for one sequential element is altered to facilitate production of the preceding and subsequent elements. Ten pianists were asked to play selected pieces with the right hand at a uniform tempo. Key-press times were recorded along with the electromyographic (EMG) activity from seven channels: thumb flexor and abductor muscles, a flexor for each finger, and the four-finger extensor muscle. For the thumb and index finger, principal components of EMG waveforms revealed highly consistent variations in the shape of the flexor bursts, depending on the type of sequence in which a particular central key press was embedded. For all digits, the duration of the central EMG burst scaled, along with slight variations across subjects in the duration of the interkeystroke intervals. Even within a narrow time frame (about 100 ms) centered on the central EMG burst, the exact balance of EMG amplitudes across multiple muscles depended on the nature of the preceding and subsequent key presses. This fails to support the idea of fixed burst patterns executed in sequential phases and instead provides evidence for neuromuscular coarticulation throughout the time course of a hand movement sequence.
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Buniya, A., Ali H. Al-Timemy, A. Aldoori, and Rami N. Khushaba. "Analysis of Different Hand and Finger Grip Patterns using Surface Electromyography and Hand Dynamometry." Al-Khwarizmi Engineering Journal 16, no. 2 (June 1, 2020): 14–23. http://dx.doi.org/10.22153/kej.2020.05.001.
Повний текст джерелаАнотація:
Recording an Electromyogram (EMG) signal is essential for diagnostic procedures like muscle health assessment and motor neurons control. The EMG signals have been used as a source of control for powered prosthetics to support people to accomplish their activities of daily living (ADLs). This work deals with studying different types of hand grips and finding their relationship with EMG activity. Five subjects carried out four functional movements (fine pinch, tripod grip and grip with the middle and thumb finger, as well as the power grip). Hand dynamometer has been used to record the EMG activity from three muscles namely; Flexor Carpi Radialis (FCR), Flexor Digitorum Superficialis (FDS), and Abductor Pollicis Brevis (ABP) with different levels of Maximum Voluntary Contraction (MVC) (10-100%). In order to analyze the collected EMG and force data, the mean absolute value of each trial is calculated followed by a calculation of the average of the 3 trials for each grip for each subject across the different MVC levels utilized in the study. Then, the mean and the standard deviation (SD) across all participants (3 males and 2 females) are calculated for FCR, FDS and APB muscles with multiple % MVC, i.e 10, 30, 50, 70 % MVC for each gesture. The results showed that APB muscle has the highest mean EMG activity across all grips, followed by FCR muscle. Furthermore, the grip with the thumb and middle fingers is the grip with the highest EMG activity for 10-70% MVC than the power grip. As for the 100% MVC, thumb and middle fingers grip achieved the highest EMG activity for APB muscle, while the power grip has the highest EMG activity for both FCR and FDS muscles.
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Lei, Yuming, and Monica A. Perez. "Phase-dependent deficits during reach-to-grasp after human spinal cord injury." Journal of Neurophysiology 119, no. 1 (January 1, 2018): 251–61. http://dx.doi.org/10.1152/jn.00542.2017.
Повний текст джерелаАнотація:
Most cervical spinal cord injuries result in asymmetrical functional impairments in hand and arm function. However, the extent to which reach-to-grasp movements are affected in humans with incomplete cervical spinal cord injury (SCI) remains poorly understood. Using kinematics and electromyographic (EMG) recordings in hand and arm muscles we studied the different phases of unilateral self-paced reach-to-grasp movements (arm acceleration, hand opening and closing) to a small cylinder in the more and less affected arms of individuals with cervical SCI and in age-matched controls. We found that SCI subjects showed prolonged movement duration in both arms during arm acceleration, and hand opening and closing compared with controls. Notably, the more affected arm showed an additional increase in movement duration at the time to close the hand compared with the less affected arm. Also, the time at which the index finger and thumb contacted the object and the variability of finger movement trajectory were increased in the more compared with the less affected arm of SCI participants. Participants with prolonged movement duration during hand closing were those with more pronounced deficits in sensory function. The muscle activation ratio between the first dorsal interosseous and abductor pollicis brevis muscles decreased during hand closing in the more compared with the less affected arm of SCI participants. Our results suggest that deficits in movement kinematics during reach-to-grasp movements are more pronounced at the time to close the hand in the more affected arm of SCI participants, likely related to deficits in EMG muscle activation and sensory function. NEW & NOTEWORTHY Humans with cervical spinal cord injury usually present asymmetrical functional impairments in hand and arm function. Here, we demonstrate for the first time that deficits in movement kinematics during reaching and grasping movements are more pronounced at the time to close the hand in the more affected arm of spinal cord injury. We suggest that this is in part related to deficits in muscle activation ratios between hand muscles and a decrease in sensory function.
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Malešević, Nebojša, Dimitrije Marković, Gunter Kanitz, Marco Controzzi, Christian Cipriani, and Christian Antfolk. "Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals." Complexity 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/9728264.
Повний текст джерелаАнотація:
We present a novel computational technique intended for the robust and adaptable control of a multifunctional prosthetic hand using multichannel surface electromyography. The initial processing of the input data was oriented towards extracting relevant time domain features of the EMG signal. Following the feature calculation, a piecewise modeling of the multidimensional EMG feature dynamics using vector autoregressive models was performed. The next step included the implementation of hierarchical hidden semi-Markov models to capture transitions between piecewise segments of movements and between different movements. Lastly, inversion of the model using an approximate Bayesian inference scheme served as the classifier. The effectiveness of the novel algorithms was assessed versus methods commonly used for real-time classification of EMGs in a prosthesis control application. The obtained results show that using hidden semi-Markov models as the top layer, instead of the hidden Markov models, ranks top in all the relevant metrics among the tested combinations. The choice of the presented methodology for the control of prosthetic hand is also supported by the equal or lower computational complexity required, compared to other algorithms, which enables the implementation on low-power microcontrollers, and the ability to adapt to user preferences of executing individual movements during activities of daily living.
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Kralikova, Ivana, Branko Babusiak, and Lubomir Kralik. "EMG SIGNALS FOR FINGER MOVEMENT CLASSIFICATION BASED ON SHORT-TERM FOURIER TRANSFORM AND DEEP LEARNING." Lékař a technika - Clinician and Technology 51, no. 1-4 (December 31, 2021): 15–20. http://dx.doi.org/10.14311/ctj.2021.1.02.
Повний текст джерелаАнотація:
An interface based on electromyographic (EMG) signals is considered one of the central fields in human-machine interface (HCI) research with broad practical use. This paper presents the recognition of 13 individual finger movements based on the time-frequency representation of EMG signals via spectrograms. A deep learning algorithm, namely a convolutional neural network (CNN), is used to extract features and classify them. Two approaches to EMG data representations are investigated: different window segmentation lengths and reduction of the measured channels. The overall highest accuracy of the classification reaches 95.5% for a segment length of 300 ms. The average accuracy attains more than 90% by reducing channels from four to three.
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Gottlieb, G. L., Q. Song, D. A. Hong, and D. M. Corcos. "Coordinating two degrees of freedom during human arm movement: load and speed invariance of relative joint torques." Journal of Neurophysiology 76, no. 5 (November 1, 1996): 3196–206. http://dx.doi.org/10.1152/jn.1996.76.5.3196.
Повний текст джерелаАнотація:
1. Eight subjects performed three series of pointing tasks with the unconstrained arm. Series one and two required subjects to move between two fixed targets as quickly as possible with different weights attached to the wrist. By specifying initial and final positions of the finger tip, the first series was performed by flexion of both shoulder and elbow and the second by shoulder flexion and elbow extension. The third series required flexion at both joints, and subjects were instructed to vary movement speed. We examined how variations in load or intended speed were associated with changes in the amount and timing of the electromyographic (EMG) activity and the net muscle torque production. 2. EMG and torque patterns at the individual joints varied with load and speed according to most of the same rules we have described for single-joint movements. 1) Movements were produced by biphasic torque pulses and biphasic or triphasic EMG bursts at both joints. 2) The accelerating impulse was proportional to the load when the subject moved “as fast and accurately as possible” or to speed if that was intentionally varied. 3) The area of the EMG bursts of agonist muscles varied with the impulse. 4) The rates of rise of the net muscle torques and of the EMG bursts were proportional to intended speed and insensitive to inertial load. 5) The areas of the antagonist muscle EMG bursts were proportional to intended movement speed but showed less dependence on load, which is unlike what is observed during single-joint movements. 3. Comparisons across joints showed that the impulse produced at the shoulder was proportional to that produced at the elbow as both varied together with load and speed. The torques at the two joints varied in close synchrony, achieving maxima and going through zero almost simultaneously. 4. We hypothesize that “coordination” of the elbow and shoulder is by the planning and generation of synchronized, biphasic muscle torque pulses that remain in near linear proportionality to each other throughout most of the movement. This linear synergy produces movements with the commonly observed kinematic properties and that are preserved over changes in speed and load.
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Taghizadeh, Zahra, Saeid Rashidi, and Ahmad Shalbaf. "Finger movements classification based on fractional Fourier transform coefficients extracted from surface EMG signals." Biomedical Signal Processing and Control 68 (July 2021): 102573. http://dx.doi.org/10.1016/j.bspc.2021.102573.
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Naik, Ganesh R., and Hung T. Nguyen. "Nonnegative Matrix Factorization for the Identification of EMG Finger Movements: Evaluation Using Matrix Analysis." IEEE Journal of Biomedical and Health Informatics 19, no. 2 (March 2015): 478–85. http://dx.doi.org/10.1109/jbhi.2014.2326660.
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Schieber, Marc H., and Gil Rivlis. "Partial Reconstruction of Muscle Activity From a Pruned Network of Diverse Motor Cortex Neurons." Journal of Neurophysiology 97, no. 1 (January 2007): 70–82. http://dx.doi.org/10.1152/jn.00544.2006.
Повний текст джерелаАнотація:
Primary motor cortex (M1) neurons traditionally have been viewed as “upper motor neurons” that directly drive spinal motoneuron pools, particularly during finger movements. We used spike-triggered averages (SpikeTAs) of electromyographic (EMG) activity to select M1 neurons whose spikes signaled the arrival of input in motoneuron pools, and examined the degree of similarity between the activity patterns of these M1 neurons and their target muscles during 12 individuated finger and wrist movements. Neuron–EMG similarity generally was low. Similarity was unrelated to the strength of the SpikeTA effect, to whether the effect was pure versus synchrony, or to the number of muscles influenced by the neuron. Nevertheless, the sum of M1 neuron activity patterns, each weighted by the sign and strength of its SpikeTA effect, could be more similar to the EMG than the average similarity of individual neurons. Significant correlations between the weighted sum of M1 neuron activity patterns and EMG were obtained in six of 17 muscles, but showed R2 values ranging from only 0.26 to 0.42. These observations suggest that additional factors—including inputs from sources other than M1 and nonlinear summation of inputs to motoneuron pools—also contributed substantially to EMG activity patterns. Furthermore, although each of these M1 neurons produced SpikeTA effects with a significant peak or trough 6–16 ms after the triggering spike, shifting the weighted sum of neuron activity to lead the EMG by 40–60 ms increased their similarity, suggesting that the influence of M1 neurons that produce SpikeTA effects includes substantial synaptic integration that in part may reach the motoneuron pools over less-direct pathways.
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Jaber, Hneen Mahdi, Mohammed A. Mohammed, and Nabel Kadhim Abd al-Sahib. "Low-Cost Prosthesis for People with Transradial Amputations." Al-Nahrain Journal for Engineering Sciences 23, no. 2 (September 18, 2020): 167–77. http://dx.doi.org/10.29194/njes.23020167.
Повний текст джерелаАнотація:
Prosthetic is an artificial tool that replaces part of the human frame absent because of ailment, damage, or distortion. The current activities in Iraq draw interest to the upper limb discipline because of the growth in variety of amputees and. It is necessary to do extensive researches in this subject to help lessen the struggling of patients. This paper describes the design and development of low-cost prosthesis for people with transradial amputations. The presented design involves a hand with five fingers moving by means of a gear box mechanism. The design of this artificial hand allows five degrees of freedom(5DOF), one degree of freedom for each finger. The artificial hand works by an actuation system (6V) Polou motor with gear ratio equal to 50:1 due to its compactness and cheapness. The designed hand was manufactured by a 3D printing process using polylacticacid material (PLA). Some experimental were accomplished using the designed hand for gripping objects. Initially the EMG signal was recorded when the muscle contracted in one second, two seconds, three seconds. The synthetic hand was able to produce range of gesture and grasping moves separately just like the actual hand by using KNN classification which are complete hand Pinch, fist, and jack chuck. The simulation of the fingers movements was achieved using ANSYS software to analysis the movement (pinch, fist, and jack chuck), obtain bested of stress influencer at each finger, and maximum deformation at each movement.
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Moran, Daniel W., and Andrew B. Schwartz. "Motor Cortical Activity During Drawing Movements: Population Representation During Spiral Tracing." Journal of Neurophysiology 82, no. 5 (November 1, 1999): 2693–704. http://dx.doi.org/10.1152/jn.1999.82.5.2693.
Повний текст джерелаАнотація:
Monkeys traced spirals on a planar surface as unitary activity was recorded from either premotor or primary motor cortex. Using the population vector algorithm, the hand's trajectory could be accurately visualized with the cortical activity throughout the task. The time interval between this prediction and the corresponding movement varied linearly with the instantaneous radius of curvature; the prediction interval was longer when the path of the finger was more curved (smaller radius). The intervals in the premotor cortex fell into two groups, whereas those in the primary motor cortex formed a single group. This suggests that the change in prediction interval is a property of a single population in primary motor cortex, with the possibility that this outcome is due to the different properties generated by the simultaneous action of separate subpopulations in premotor cortex. Electromyographic (EMG) activity and joint kinematics were also measured in this task. These parameters varied harmonically throughout the task with many of the same characteristics as those of single cortical cells. Neither the lags between joint-angular velocities and hand velocity nor the lags between EMG and hand velocity could explain the changes in prediction interval between cortical activity and hand velocity. The simple spatial and temporal relationship between cortical activity and finger trajectory suggests that the figural aspects of this task are major components of cortical activity.
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Furui, Akira, Shintaro Eto, Kosuke Nakagaki, Kyohei Shimada, Go Nakamura, Akito Masuda, Takaaki Chin, and Toshio Tsuji. "A myoelectric prosthetic hand with muscle synergy–based motion determination and impedance model–based biomimetic control." Science Robotics 4, no. 31 (June 26, 2019): eaaw6339. http://dx.doi.org/10.1126/scirobotics.aaw6339.
Повний текст джерелаАнотація:
Prosthetic hands are prescribed to patients who have suffered an amputation of the upper limb due to an accident or a disease. This is done to allow patients to regain functionality of their lost hands. Myoelectric prosthetic hands were found to have the possibility of implementing intuitive controls based on operator’s electromyogram (EMG) signals. These controls have been extensively studied and developed. In recent years, development costs and maintainability of prosthetic hands have been improved through three-dimensional (3D) printing technology. However, no previous studies have realized the advantages of EMG-based classification of multiple finger movements in conjunction with the introduction of advanced control mechanisms based on human motion. This paper proposes a 3D-printed myoelectric prosthetic hand and an accompanying control system. The muscle synergy–based motion-determination method and biomimetic impedance control are introduced in the proposed system, enabling the classification of unlearned combined motions and smooth and intuitive finger movements of the prosthetic hand. We evaluate the proposed system through operational experiments performed on six healthy participants and an upper-limb amputee participant. The experimental results demonstrate that our prosthetic hand system can successfully classify both learned single motions and unlearned combined motions from EMG signals with a high degree of accuracy. Furthermore, applications to real-world uses of prosthetic hands are demonstrated through control tasks conducted by the amputee participant.
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Deutsch, Katherine M., John A. Stephens, and Simon F. Farmer. "Developmental profile of slow hand movement oscillation coupling in humans." Journal of Neurophysiology 105, no. 5 (May 2011): 2204–12. http://dx.doi.org/10.1152/jn.00695.2010.
Повний текст джерелаАнотація:
In adults, slow hand and finger movements are characterized by 6- to 12-Hz discontinuities visible in the raw records and spectra of motion signals such as acceleration. This pulsitile behavior is correlated with motor unit synchronization at 6–12 Hz as shown by significant coherence at these frequencies between pairs of motor units and between the motor units and the acceleration recorded from the limb part controlled by the muscle, suggesting that it has a central origin. In this study, we examined the correlation between this 6- to 12-Hz pulsatile behavior and muscle activity as a function of childhood development. Sixty-eight participants (ages 4–25 yr) performed static wrist extensions against gravity or slow wrist extension and flexion movements while extensor carpi radialis muscle electromyographic (EMG) and wrist acceleration signals were simultaneously recorded. Coherence between EMG and acceleration within the 6- to 12-Hz frequency band was used as an index of the strength of the relation between central drive and the motor output. The main findings of the study are 1) EMG-acceleration coherence increased with increases in age, with the age differences being greater under movement conditions and the difference between conditions increasing with age; 2) the EMG signal showed increases in normalized power with increases in age under both conditions; and 3) coherence under movement conditions was moderately positively correlated with manual dexterity. These findings indicate that the strength of the 6- to 12-Hz central oscillatory drive to the motor output increases through childhood development and may contribute to age-related improvements in motor skills.
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Lucas, Lenny, Matthew DiCicco, and Yoky Matsuoka. "An EMG-Controlled Hand Exoskeleton for Natural Pinching." Journal of Robotics and Mechatronics 16, no. 5 (October 20, 2004): 482–88. http://dx.doi.org/10.20965/jrm.2004.p0482.
Повний текст джерелаАнотація:
Spinal cord and other local injuries often lead to partial paralysis while the brain stays fully functional. When this partial paralysis occurs in the hand, these individuals are not able to execute daily activities on their own even if their arms are functional. To remedy this problem, a lightweight, low-profile orthotic exoskeleton has been designed to restore dexterity to paralyzed hands. The exoskeleton’s movements are controlled by the user’s available electromyography (EMG) signals. The device has two actuators controlling the index finger flexion that can be used to perform a pinching motion against a fixed thumb. Using this orthotic device, a new control technique was developed to allow for a natural reaching and pinching sequence by utilizing the natural residual muscle activation patterns. To design this controller, two actuator control algorithms were explored with a quadriplegic (C5/C6) subject and it was determined that a simple binary control algorithm allowed for faster interaction with objects over a variable control algorithm. The binary algorithm was then used as an enabling algorithm to activate the exoskeleton movements when the natural sequence of muscle activities found a pattern related to a pinch. This natural pinching technique has shown significant promise toward realistic neural control of wearable robotic devices to assist paralyzed individuals.
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Zhang, Ting, Xin Qing Wang, Li Jiang, Xinyu Wu, Wei Feng, Dingjiang Zhou, and Hong Liu. "Biomechatronic design and control of an anthropomorphic artificial hand for prosthetic applications." Robotica 34, no. 10 (January 23, 2015): 2291–308. http://dx.doi.org/10.1017/s0263574714002902.
Повний текст джерелаАнотація:
SUMMARYIn this paper, we propose a biomechatronic design of an anthropomorphic artificial hand that is able to mimic the natural motion of human fingers. The prosthetic hand has 5 fingers and 15 joints, which are actuated by 5 embedded motors. Each finger has three phalanges that can fulfill flexion-extension movements independently. The thumb is specially designed to move along a cone surface when grasping, and the other four fingers are well developed based on the four-bar link mechanism to imitate the motion of the human finger. To accomplish the sophisticated control schemes, the fingers are equipped with numerous torque and position sensors. The mechanical parts, sensors, and motion control systems are integrated in the hand structure, and the motion of the hand can be controlled through electromyography (EMG) signals in real-time. A new concept for the sensory feedback system based on an electrical stimulator is also taken into account. The low-cost prosthetic hand is small in size (85% of the human hand), of low weight (420 g) and has a large grasp power (10 N on the fingertips), hence it has a dexterous and humanlike appearance. The performance of the prosthetic hand is validated in a clinical evaluation on transradial amputees.
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Bennett, K. M., and R. N. Lemon. "Corticomotoneuronal contribution to the fractionation of muscle activity during precision grip in the monkey." Journal of Neurophysiology 75, no. 5 (May 1, 1996): 1826–42. http://dx.doi.org/10.1152/jn.1996.75.5.1826.
Повний текст джерелаАнотація:
1. During independent finger movements, the intrinsic muscles of the hand show a fractionated pattern of activity in which the timing and amplitude of electromyographic (EMG) activity varies considerably from one muscle to another. It has been suggested that, in the macaque monkey, corticomotoneuronal (CM) cells that produce postspike facilitation (PSF) of EMG in these muscles contribute to this fractionation. To test this hypothesis, we have investigated the relationship between the pattern of PSF exerted by a CM cell and the pattern of activity shown by the cell and by its target muscles. 2. The activity of 15 identified CM cells was recorded from two monkeys that performed a precision grip task. Spike-triggered averaging of rectified EMG during the hold period of this task showed that each cell produced PSF in at least two intrinsic hand muscles. 3. Segments of data were selected from the initial movement period of the task in which the EMG activity in one target muscle was substantially greater than that of the other, and the mean firing rate of each CM cell was determined for these periods. 4. CM cells showed bursts of activity in the movement period. Most of them (13/15) had a significantly (P < 0.001) higher firing rate when one of its target muscles was more active than the other. For nine of these cells (identified as set A), this muscle was the one receiving the larger PSF. In four cases (set B), the reverse was true. Two cells (set C), which produced PSF of equal size in their target muscles, showed no change in firing rate across the periods of fractionated EMG activity. 5. All set A and set B cells fired at significantly (P < 0.001) higher rates during the movement period, in association with fractionation of EMG activity, than in the hold period, in which a cocontracted pattern of muscle activity was observed. 6. There were pronounced differences in the strength of PSF exerted by the CM cells on their target muscles during the fractionation periods. One CM cell exerted PSF of a muscle during one period of fractionation, but postspike suppression of the same muscle during the other period. 7. It is suggested that changes in the firing rate of a CM cell and in the degree of facilitation it exerts could both contribute to the fractionation of activity in its target muscles. Cells of set A appear to be specifically recruited in a manner that directly reflects the pattern of facilitation they exert on the sampled target muscles. These results may explain why the CM system is so important for the performance of relatively independent finger movements.
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Lalitha, Anusha, and Nitish V. Thakor. "Design of an Accelerometer-Controlled Myoelectric Human Computer Interface." Advanced Materials Research 403-408 (November 2011): 3973–79. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3973.
Повний текст джерелаАнотація:
The purpose of this study is to develop an alternate in-air input device which is intended to make interaction with computers easier for amputees. This paper proposes the design and utility of accelerometer controlled Myoelectric Human Computer Interface (HCI). This device can function as a PC mouse. The two dimensional position control of the mouse cursor is done by an accelerometer-based method. The left click and right click and other extra functions of this device are controlled by the Electromyographic (EMG) signals. Artificial Neural Networks (ANNs) are used to decode the intended movements during run-time. ANN is a pattern recognition based classification. An amputee can control it using phantom wrist gestures or finger movements.
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Schwartz, Andrew B., and Daniel W. Moran. "Motor Cortical Activity During Drawing Movements: Population Representation During Lemniscate Tracing." Journal of Neurophysiology 82, no. 5 (November 1, 1999): 2705–18. http://dx.doi.org/10.1152/jn.1999.82.5.2705.
Повний текст джерелаАнотація:
Activity was recorded extracellularly from single cells in motor and premotor cortex as monkeys traced figure-eights on a touch-sensitive computer monitor using the index finger. Each unit was recorded individually, and the responses collected from four hemispheres (3 primary motor and 1 dorsal premotor) were analyzed as a population. Population vectors constructed from this activity accurately and isomorphically represented the shape of the drawn figures showing that they represent the spatial aspect of the task well. These observations were extended by examining the temporal relation between this neural representation and finger displacement. Movements generated during this task were made in four kinematic segments. This segmentation was clearly evident in a time series of population vectors. In addition, the [Formula: see text] power law described for human drawing was also evident in the neural correlate of the monkey hand trajectory. Movement direction and speed changed continuously during the task. Within each segment, speed and direction changed reciprocally. The prediction interval between the population vector and movement direction increased in the middle of the segments where curvature was high, but decreased in straight portions at the beginning and end of each segment. In contrast to direction, prediction intervals between the movement speed and population vector length were near-constant with only a modest modulation in each segment. Population vectors predicted direction (vector angle) and speed (vector length) throughout the drawing task. Joint angular velocity and arm muscle EMG were well correlated to hand direction, suggesting that kinematic and kinetic parameters are correlated in these tasks.
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Alizadeh-Meghrazi, Milad, Gurjant Sidhu, Saransh Jain, Michael Stone, Ladan Eskandarian, Amirali Toossi, and Milos R. Popovic. "A Mass-Producible Washable Smart Garment with Embedded Textile EMG Electrodes for Control of Myoelectric Prostheses: A Pilot Study." Sensors 22, no. 2 (January 15, 2022): 666. http://dx.doi.org/10.3390/s22020666.
Повний текст джерелаАнотація:
Electromyography (EMG) is the resulting electrical signal from muscle activity, commonly used as a proxy for users’ intent in voluntary control of prosthetic devices. EMG signals are recorded with gold standard Ag/AgCl gel electrodes, though there are limitations in continuous use applications, with potential skin irritations and discomfort. Alternative dry solid metallic electrodes also face long-term usability and comfort challenges due to their inflexible and non-breathable structures. This is critical when the anatomy of the targeted body region is variable (e.g., residual limbs of individuals with amputation), and conformal contact is essential. In this study, textile electrodes were developed, and their performance in recording EMG signals was compared to gel electrodes. Additionally, to assess the reusability and robustness of the textile electrodes, the effect of 30 consumer washes was investigated. Comparisons were made between the signal-to-noise ratio (SNR), with no statistically significant difference, and with the power spectral density (PSD), showing a high correlation. Subsequently, a fully textile sleeve was fabricated covering the forearm, with 14 textile electrodes. For three individuals, an artificial neural network model was trained, capturing the EMG of 7 distinct finger movements. The personalized models were then used to successfully control a myoelectric prosthetic hand.
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Sezgin, Necmettin. "A new hand finger movements’ classification system based on bicoherence analysis of two-channel surface EMG signals." Neural Computing and Applications 31, no. 8 (November 20, 2017): 3327–37. http://dx.doi.org/10.1007/s00521-017-3286-z.
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Proteau, Rose-Ange. "Ergonomics in the Dental Clinic." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 26 (July 2000): 197–200. http://dx.doi.org/10.1177/154193120004402616.
Повний текст джерелаАнотація:
A number of dental hygienists have developed pathologies that cause them to be absent from work for long periods of time, and making it difficult for them to return to work. Hygienists' work involves extended static muscular effort in the neck and pectoral girdle, combined with recurrent dynamic movements of the wrist and fingers, associated with efforts to remove tartar from the teeth. Over the last two years, a dozen dental hygienists have consulted us for various shoulder, elbow, wrist, hand and finger problems. Changes in methods, instruments, equipment and the environment have allowed hygienists to adopt safer working positions. Reduced muscular activity was confirmed by EMG testing. The use of telescopic pivoting armrests has facilitated the adoption of new working methods by dental hygienists, and also provided needed arm support. A new concept for a pivoting armrest has been developed with round gel elbows-rests.
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Calabro, Finnegan J., and Monica A. Perez. "Bilateral reach-to-grasp movement asymmetries after human spinal cord injury." Journal of Neurophysiology 115, no. 1 (January 1, 2016): 157–67. http://dx.doi.org/10.1152/jn.00692.2015.
Повний текст джерелаАнотація:
Cervical spinal cord injury (SCI) in humans typically damages both sides of the spinal cord, resulting in asymmetric functional impairments in the arms. Despite this well-accepted notion and the growing emphasis on the use of bimanual training strategies, how movement of one arm affects the motion of the contralateral arm after SCI remains unknown. Using kinematics and multichannel electromyographic (EMG) recordings we studied unilateral and bilateral reach-to-grasp movements to a small and a large cylinder in individuals with asymmetric arm impairments due to cervical SCI and age-matched control subjects. We found that the stronger arm of SCI subjects showed movement durations longer than control subjects during bilateral compared with unilateral trials. Specifically, movement duration was prolonged when opening and closing the hand when reaching for a large and a small object, respectively, accompanied by deficient activation of finger flexor and extensor muscles. In subjects with SCI interlimb coordination was reduced compared with control subjects, and individuals with lesser coordination between hands were those who showed prolonged times to open the hand. Although the weaker arm showed movement durations during bilateral compared with unilateral trials that were proportional to controls, the stronger arm was excessively delayed during bilateral reaching. Altogether, our findings demonstrate that during bilateral reach-to-grasp movements the more impaired arm has detrimental effects on hand opening and closing of the less impaired arm and that they are related, at least in part, to deficient control of EMG activity of hand muscles. We suggest that hand opening might provide a time to drive bimanual coordination adjustments after human SCI.
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Rahim, Md, and Jungpil Shin. "Hand Movement Activity-Based Character Input System on a Virtual Keyboard." Electronics 9, no. 5 (May 8, 2020): 774. http://dx.doi.org/10.3390/electronics9050774.
Повний текст джерелаАнотація:
Nowadays, gesture-based technology is revolutionizing the world and lifestyles, and the users are comfortable and care about their needs, for example, in communication, information security, the convenience of day-to-day operations and so forth. In this case, hand movement information provides an alternative way for users to interact with people, machines or robots. Therefore, this paper presents a character input system using a virtual keyboard based on the analysis of hand movements. We analyzed the signals of the accelerometer, gyroscope, and electromyography (EMG) for movement activity. We explored potential features of removing noise from input signals through the wavelet denoising technique. The envelope spectrum is used for the analysis of the accelerometer and gyroscope and cepstrum for the EMG signal. Furthermore, the support vector machine (SVM) is used to train and detect the signal to perform character input. In order to validate the proposed model, signal information is obtained from predefined gestures, that is, “double-tap”, “hold-fist”, “wave-left”, “wave-right” and “spread-finger” of different respondents for different input actions such as “input a character”, “change character”, “delete a character”, “line break”, “space character”. The experimental results show the superiority of hand gesture recognition and accuracy of character input compared to state-of-the-art systems.
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Cramer, Steven C., Keith C. Stegbauer, Angela Mark, Robert Price, Kristin Barquist, Kathleen R. Bell, Peter C. Esselman, Ib R. Odderson, and Kenneth R. Maravilla. "Motor cortex activation in hemiparetic stroke patients." Stroke 32, suppl_1 (January 2001): 334. http://dx.doi.org/10.1161/str.32.suppl_1.334-c.
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100 Little is known about the function of surviving motor cortex after hemiparetic stroke. Though the corticospinal tract may be damaged, function may persist via intact intracortical connections. We probed motor cortex function using paradigms unrelated to genesis of paretic limb movement. Seven patients with chronic post-stroke hemiparesis, including total hand plegia, were studied with functional MRI (fMRI). Brain activation was achieved by alternating between rest and one of several stimuli. For the plegic hand, stimuli were passive index finger movement, or viewing active movements; for the non-plegic hand, active or passive index finger movement. Brain activation maps (p<.001) were generated, after which anatomical landmarks were used to identify regions of interest within non-infarcted tissue. Tasks were rehearsed before fMRI, during which surface EMG leads were placed on 5 muscles in each arm. Patients were median 5 months post-stroke, median age 66 years. Median NIH stroke scale score was 9; Rankin, 3; and arm motor Fugl-Meyer score, 18 (normal=66); Motor Activity Log confirmed no plegic hand use. Studies with excess head movement were excluded, including all plegic hand tasks for 1 patient. Plegic hand tasks (10 studies across 6 patients) activated the stroke hemisphere in all patients, including primary motor cortex (5 patients), primary sensory cortex (5 patients), premotor cortex (4 patients), and supplementary motor area (3 patients). Non-stroke hemisphere was also activated, particularly primary motor cortex (5 patients). In a few instances, EMG disclosed paretic arm muscle activity, but this had no relationship to fMRI activation. Non-plegic hand tasks (9 studies across 7 patients) activated the stroke hemisphere ipsilaterally, including supplementary motor area in all 7 patients, and primary motor cortex in 6 patients. In patients with post-stroke hemiparesis, passive stimulation activates surviving motor cortex regions within the stroke-affected hemisphere. After corticospinal tract damage, motor cortex can still be activated during tasks unrelated to paretic limb movement. The results may suggest therapeutic avenues for improving motor function after stroke.
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Antonelli, Michele Gabrio, Pierluigi Beomonte Zobel, Francesco Durante, and Mohammad Zeer. "Modeling-Based EMG Signal (MBES) Classifier for Robotic Remote-Control Purposes." Actuators 11, no. 3 (February 22, 2022): 65. http://dx.doi.org/10.3390/act11030065.
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The fast-growing human–robot collaboration predicts that a human operator could command a robot without mechanical interface if effective communication channels are established. In noisy, vibrating and light sensitive environments, some sensors for detecting the human intention could find critical issues to be adopted. On the contrary, biological signals, as electromyographic (EMG) signals, seem to be more effective. In order to command a laboratory collaborative robot powered by McKibben pneumatic muscles, promising actuators for human–robot collaboration due to their inherent compliance and safety features have been researched, a novel modeling-based electromyographic signal (MBES) classifier has been developed. It is based on one EMG sensor, a Myotrac one, an Arduino Uno and a proper code, developed in the Matlab environment, that performs the EMG signal recognition. The classifier can recognize the EMG signals generated by three hand-finger movements, regardless of the amplitude and time duration of the signal and the muscular effort, relying on three mathematical models: exponential, fractional and Gaussian. These mathematical models have been selected so that they are the best fitting with the EMG signal curves. Each of them can be assigned a consent signal for performing the wanted pick-and-place task by the robot. An experimental activity was carried out to test and achieve the best performance of the classifier. The validated classifier was applied for controlling three pressure levels of a McKibben-type pneumatic muscle. Encouraging results suggest that the developed classifier can be a valid command interface for robotic purposes.
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Gao, Zhaolong, Rongyu Tang, Qiang Huang, and Jiping He. "A Multi-DoF Prosthetic Hand Finger Joint Controller for Wearable sEMG Sensors by Nonlinear Autoregressive Exogenous Model." Sensors 21, no. 8 (April 7, 2021): 2576. http://dx.doi.org/10.3390/s21082576.
Повний текст джерелаАнотація:
The loss of mobility function and sensory information from the arm, hand, and fingertips hampers the activities of daily living (ADL) of patients. A modern bionic prosthetic hand can compensate for the lost functions and realize multiple degree of freedom (DoF) movements. However, the commercially available prosthetic hands usually have limited DoFs due to limited sensors and lack of stable classification algorithms. This study aimed to propose a controller for finger joint angle estimation by surface electromyography (sEMG). The sEMG data used for training were gathered with the Myo armband, which is a commercial EMG sensor. Two features in the time domain were extracted and fed into a nonlinear autoregressive model with exogenous inputs (NARX). The NARX model was trained with pre-selected parameters using the Levenberg–Marquardt algorithm. Comparing with the targets, the regression correlation coefficient (R) of the model outputs was more than 0.982 over all test subjects, and the mean square error was less than 10.02 for a signal range in arbitrary units equal to [0, 255]. The study also demonstrated that the proposed model could be used in daily life movements with good accuracy and generalization abilities.
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Naik, Ganesh R., Kerry G. Baker, and Hung T. Nguyen. "Dependence Independence Measure for Posterior and Anterior EMG Sensors Used in Simple and Complex Finger Flexion Movements: Evaluation Using SDICA." IEEE Journal of Biomedical and Health Informatics 19, no. 5 (September 2015): 1689–96. http://dx.doi.org/10.1109/jbhi.2014.2340397.
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Guettler, Knut. "Electromyography and Muscle Activities in Double Bass Playing." Music Perception 9, no. 3 (1992): 303–9. http://dx.doi.org/10.2307/40285554.
Повний текст джерелаАнотація:
Two electromyography (EMG) experiments related to double bass playing are described. By means of surface electrodes and synchronous recordings of finger movements on the string, it is shown in the first experiment that the production of vibrato is associated with pulsating contractions of two back muscles, the teres minor and the teres major. A failure in the coordination of the activity of these muscles was observed in a student having problems in producing a proper vibrato. The second experiment demonstrates clear effects of a small change in the holding of the instrument on muscular stress in the left and right trapezius muscles. These changes were revealed by the occurrence of different degrees of muscular force developed during playing. The pedagogical implications of these findings are discussed.
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Zhou, Ping, Madeleine M. Lowery, Kevin B. Englehart, He Huang, Guanglin Li, Levi Hargrove, Julius P. A. Dewald, and Todd A. Kuiken. "Decoding a New Neural–Machine Interface for Control of Artificial Limbs." Journal of Neurophysiology 98, no. 5 (November 2007): 2974–82. http://dx.doi.org/10.1152/jn.00178.2007.
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An analysis of the motor control information content made available with a neural–machine interface (NMI) in four subjects is presented in this study. We have developed a novel NMI–called targeted muscle reinnervation (TMR)—to improve the function of artificial arms for amputees. TMR involves transferring the residual amputated nerves to nonfunctional muscles in amputees. The reinnervated muscles act as biological amplifiers of motor commands in the amputated nerves and the surface electromyogram (EMG) can be used to enhance control of a robotic arm. Although initial clinical success with TMR has been promising, the number of degrees of freedom of the robotic arm that can be controlled has been limited by the number of reinnervated muscle sites. In this study we assess how much control information can be extracted from reinnervated muscles using high-density surface EMG electrode arrays to record surface EMG signals over the reinnervated muscles. We then applied pattern classification techniques to the surface EMG signals. High accuracy was achieved in the classification of 16 intended arm, hand, and finger/thumb movements. Preliminary analyses of the required number of EMG channels and computational demands demonstrate clinical feasibility of these methods. This study indicates that TMR combined with pattern-recognition techniques has the potential to further improve the function of prosthetic limbs. In addition, the results demonstrate that the central motor control system is capable of eliciting complex efferent commands for a missing limb, in the absence of peripheral feedback and without retraining of the pathways involved.
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Lv, Ying, Qingli Zheng, Xiubin Chen, Yi Jia, Chunsheng Hou, and Meiwen An. "Analysis on Muscle Forces of Extrinsic Finger Flexors and Extensors in Flexor Movements with sEMG and Ultrasound." Mathematical Problems in Engineering 2022 (May 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/7894935.
Повний текст джерелаАнотація:
The coupling relationship between surface electromyography (sEMG) signals and muscle forces or joint moments is the basis for sEMG applications in medicine, rehabilitation, and sports. The solution of muscle forces is the key issue. sEMG and Muscle-Tendon Junction (MTJ) displacements of the flexor digitorum superficialis (FDS), flexor digitorum profundus (FDP), and extensor digitorum (ED) were measured during five sets of finger flexion movements. Meanwhile, the muscle forces of FDS, FDP, and ED were calculated by the Finite Element Digital Human Hand Model (FE-DHHM) driven by MTJ displacements. The results showed that, in the initial position of the flexion without resistance, the high-intensity contraction of the ED kept the palm straight and the FDS was involved. The sEMG-force relationship of FDS was linear during the flexion with resistance, while FDP showed a larger sEMG amplitude than FDS, with no obvious linearity with its muscle forces. sEMG-MTJ displacement relationships for FDS and FDP were consistent with the trend of their own sEMG-force relationships. sEMG of ED decreased and then increased during the flexion with resistance, with no obvious linear relationship with muscle forces. The analysis of the proportion of muscle force and integrated EMG (iEMG) reflected the different activation patterns of FDS and ED.
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Giesebrecht, Sabine, Hiske van Duinen, Gabrielle Todd, Simon C. Gandevia, and Janet L. Taylor. "Training in a ballistic task but not a visuomotor task increases responses to stimulation of human corticospinal axons." Journal of Neurophysiology 107, no. 9 (May 1, 2012): 2485–92. http://dx.doi.org/10.1152/jn.01117.2010.
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Short periods of training in motor tasks can increase motor cortical excitability. This study investigated whether changes also occur at a subcortical level. Subjects trained in ballistic finger abduction or visuomotor tracking. The right index finger rotated around the metacarpophalangeal (MCP) joint in a splint. Surface EMG was recorded from the first dorsal interosseous. Transcranial magnetic stimulation over the back of the head (double-cone coil) elicited cervicomedullary motor evoked potentials (CMEPs) by stimulation of corticospinal axons. Responses were recorded from the relaxed muscle before, between, and after two sets of training. In study 1 ( n = 7), training comprised two sets of 150 maximal finger abductions. Feedback of acceleration was provided. With training, acceleration increased significantly. CMEPs increased to 248 ± 152% (± SD) of baseline immediately after training ( P = 0.007) but returned to control level (155 ± 141%) 10 min later. In study 2 ( n = 7), subjects matched MCP joint angle to a target path on a computer screen. After ∼30 min of training, tracking improved as shown by increased correlation between joint angle and the target pathway, reduced time lag, and reduced EMGrms. However, CMEPs remained unchanged. These results show that transmission through the corticospinal pathway at a spinal level increased after repeated ballistic movements but not after training in a visuomotor task. Thus, changes at a spinal level may contribute to improved performance in some motor tasks.
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Schneider, Hartmut, Syuyumbeki Mueller, Maryanne Oloo, Nicola Haisma, Martine Knoops-Borm, Mariele Stockhoff, Marij Tijssen, Pieter Ermers, Ruben DeFrancisco, and Steven Coughlin. "0507 A wireless patch-based polysomnography system for conducting in-lab sleep studies." SLEEP 46, Supplement_1 (May 1, 2023): A224—A225. http://dx.doi.org/10.1093/sleep/zsad077.0507.
Повний текст джерелаАнотація:
Abstract Introduction Current attended in-lab polysomnographic sleep studies are time-consuming and costly, primarily due to the time required to “hook-up” a patient to multiple electrodes and sensors and meet the quality assurance and safety requirements of an in-lab study. We developed a wireless PSG system (Onera STS - Onera Health, NL) consisting of four disposable patches and reusable pods to record full polysomnography that may reduce hook-up time while preserving standards of an attended polysomnography in the lab. Methods We used the Onera STS system for monitoring EEG, EOG, EMG, forehead reflectance SaO2, ECG, bioimpedance derived respiratory airflow and effort, airflow via nasal cannula, snoring sounds, body position, actigraphy, and leg movements, and accessory online monitoring of finger SaO2, ECG, nasal cannula airflow and video via RemLogic 4.0 or REM logic MPR system for Q/A and safety monitoring. Seventeen subjects (8 male, 9 female, age 18-to-75 yrs, BMI 29.9±6.0 kg/m2) were monitored for the evaluation of sleep apnea. We measured hook-up times and observed oxygen saturation and cardiac rhythm throughout the night. Results Mean hook-up time for the Onera STS was 5:22±1:17 minutes and for the additional on-line sensors (Finger SaO2, ECG and nasal cannula) was 3:15±1:10 minutes, resulting in an average hook-up time of less than 10 minutes. Onera PSG data revealed a mean oxygen desaturation event rate of >3% (ODI3) of 8.8 (SD 18.6) and a mean fall of oxygen saturation (ΔSaO2/event) of 4.4 (SD 1.2). The accessory online SaO2, ECG and video monitoring showed that no subjects demonstrated sustained nocturnal hypoxia, severe cardiac arrhythmia or parasomnia events that would have required interventions. Conclusion The Onera STS system substantially lowers the burden to conduct attended polysomnographic sleep studies. In combination with standard monitoring of SaO2, ECG and video, it meets the safety requirements for attended sleep studies while reducing the overall operational and capital equipment costs. The ease of application together with the reduced hook-up time makes it now possible to implement polysomnographic sleep studies in the hospital setting, particularly in addition to conventional bedside monitoring units such as ICU-, step-down unit- or hospital beds. Support (if any)
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Ferrarin, Maurizio, Marco Rabuffetti, Marina Ramella, Maurizio Osio, Enrico Mailland, and Rosa Maria Converti. "Does Instrumented Movement Analysis Alter, Objectively Confirm, or Not Affect Clinical Decision-making in Musicians with Focal Dystonia?" Medical Problems of Performing Artists 23, no. 3 (September 1, 2008): 99–106. http://dx.doi.org/10.21091/mppa.2008.3021.
Повний текст джерелаАнотація:
Focal dystonia (FD) is a movement disorder that frequently affects instrumental musicians. Distinguishing between primary dystonic movement and secondary compensatory abnormal movement is crucial for the correct treatment planning in FD. Such distinction is complex in musicians because of the complexity, speed, and smallness of involved movement. The goal of the current study was to assess the influence of instrumented movement analysis (MA) in treatment decision-making in musician's FD. A group of 18 musicians with FD was instrumentally analyzed in an MA laboratory equipped with optoelectronic and electromyographic (EMG) acquisition systems. The muscle(s) primarily responsible for the dystonic movement or posture (trigger muscle) was identified on the basis of clinical assessment alone and, in a second phase, with the additional information provided by instrumented assessment. Comparison between clinical and instrumented assessment outcomes and the subjective rating of found differences were then analyzed. In 67% of patients, instrumental assessment changed the decision made by clinical assessment, indicating identification of a different trigger muscle or allowing for a more specific identification. In 28% of patients, instrumental assessment confirmed the outcome of the clinical assessment, with an increase in the confidence level of the clinical decision. The most frequent change was an improved specification of which finger flexor muscle (superficialis or profundus) was triggering the dystonic movement. Although caution is needed due to the non-blinded design of the present study, our results suggest that instrumented movement analysis is a useful complementary tool to clinical assessment in treatment planning for musician's focal dystonia—its use might change the identification of the muscles primarily responsible for dystonic movements as well as increase the confidence level of the clinician in treatment decision-making.
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Gannouni, Sofien, Kais Belwafi, Hatim Aboalsamh, Ziyad AlSamhan, Basel Alebdi, Yousef Almassad, and Homoud Alobaedallah. "EEG-Based BCI System to Detect Fingers Movements." Brain Sciences 10, no. 12 (December 10, 2020): 965. http://dx.doi.org/10.3390/brainsci10120965.
Повний текст джерелаАнотація:
The advancement of assistive technologies toward the restoration of the mobility of paralyzed and/or amputated limbs will go a long way. Herein, we propose a system that adopts the brain-computer interface technology to control prosthetic fingers with the use of brain signals. To predict the movements of each finger, complex electroencephalogram (EEG) signal processing algorithms should be applied to remove the outliers, extract features, and be able to handle separately the five human fingers. The proposed method deals with a multi-class classification problem. Our machine learning strategy to solve this problem is built on an ensemble of one-class classifiers, each of which is dedicated to the prediction of the intention to move a specific finger. Regions of the brain that are sensitive to the movements of the fingers are identified and located. The average accuracy of the proposed EEG signal processing chain reached 81% for five subjects. Unlike the majority of existing prototypes that allow only one single finger to be controlled and only one movement to be performed at a time, the system proposed will enable multiple fingers to perform movements simultaneously. Although the proposed system classifies five tasks, the obtained accuracy is too high compared with a binary classification system. The proposed system contributes to the advancement of a novel prosthetic solution that allows people with severe disabilities to perform daily tasks in an easy manner.
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Štastný, Jakub, and Pavel Sovka. "High-Resolution Movement EEG Classification." Computational Intelligence and Neuroscience 2007 (2007): 1–12. http://dx.doi.org/10.1155/2007/54925.
Повний текст джерелаАнотація:
The aim of the contribution is to analyze possibilities of high-resolution movement classification using human EEG. For this purpose, a database of the EEG recorded during right-thumb and little-finger fast flexion movements of the experimental subjects was created. The statistical analysis of the EEG was done on the subject's basis instead of the commonly used grand averaging. Statistically significant differences between the EEG accompanying movements of both fingers were found, extending the results of other so far published works. The classifier based on hidden Markov models was able to distinguish between movement and resting states (classification score of 94–100%), but it was unable to recognize the type of the movement. This is caused by the large fraction of other (nonmovement related) EEG activities in the recorded signals. A classification method based on advanced EEG signal denoising is being currently developed to overcome this problem.
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Rashid, Nasir, Javaid Iqbal, Amna Javed, Mohsin I. Tiwana, and Umar Shahbaz Khan. "Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis." BioMed Research International 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/2695106.
Повний текст джерелаАнотація:
Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement). Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD) was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system) from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves) and Beta Rhythms (8–30 Hz) containing most of the movement data were retained through filtering using “Arduino Uno” microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%.
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Cabaraux, Pierre, Jordi Gandini, Shinji Kakei, Mario Manto, Hiroshi Mitoma, and Hirokazu Tanaka. "Dysmetria and Errors in Predictions: The Role of Internal Forward Model." International Journal of Molecular Sciences 21, no. 18 (September 20, 2020): 6900. http://dx.doi.org/10.3390/ijms21186900.
Повний текст джерелаАнотація:
The terminology of cerebellar dysmetria embraces a ubiquitous symptom in motor deficits, oculomotor symptoms, and cognitive/emotional symptoms occurring in cerebellar ataxias. Patients with episodic ataxia exhibit recurrent episodes of ataxia, including motor dysmetria. Despite the consensus that cerebellar dysmetria is a cardinal symptom, there is still no agreement on its pathophysiological mechanisms to date since its first clinical description by Babinski. We argue that impairment in the predictive computation for voluntary movements explains a range of characteristics accompanied by dysmetria. Within this framework, the cerebellum acquires and maintains an internal forward model, which predicts current and future states of the body by integrating an estimate of the previous state and a given efference copy of motor commands. Two of our recent studies experimentally support the internal-forward-model hypothesis of the cerebellar circuitry. First, the cerebellar outputs (firing rates of dentate nucleus cells) contain predictive information for the future cerebellar inputs (firing rates of mossy fibers). Second, a component of movement kinematics is predictive for target motions in control subjects. In cerebellar patients, the predictive component lags behind a target motion and is compensated with a feedback component. Furthermore, a clinical analysis has examined kinematic and electromyography (EMG) features using a task of elbow flexion goal-directed movements, which mimics the finger-to-nose test. Consistent with the hypothesis of the internal forward model, the predictive activations in the triceps muscles are impaired, and the impaired predictive activations result in hypermetria (overshoot). Dysmetria stems from deficits in the predictive computation of the internal forward model in the cerebellum. Errors in this fundamental mechanism result in undershoot (hypometria) and overshoot during voluntary motor actions. The predictive computation of the forward model affords error-based motor learning, coordination of multiple degrees of freedom, and adequate timing of muscle activities. Both the timing and synergy theory fit with the internal forward model, microzones being the elemental computational unit, and the anatomical organization of converging inputs to the Purkinje neurons providing them the unique property of a perceptron in the brain. We propose that motor dysmetria observed in attacks of ataxia occurs as a result of impaired predictive computation of the internal forward model in the cerebellum.