scholarly journals OpenSim Visualization of the Classification of Finger Movements Based on Electromyography Signal as the Single-Input Variable during Predefined Movements

2021 ◽  
Vol 10 (1) ◽  
pp. 7
Author(s):  
Jose A. Amezquita-Garcia ◽  
Miguel E. Bravo-Zanoguera ◽  
Roberto L. Avitia ◽  
Marco A. Reyna ◽  
Daniel Cuevas-González
Keyword(s):  
Hand Movement ◽  
Classification Performance ◽  
Simulation Software ◽  
Classification Error ◽  
Finger Movements ◽  
Input Devices ◽  
Hand Motion ◽  
Human Computer Interfaces ◽  
Biomechanical Simulation ◽  
Single Input

A classifier is commonly generated for multifunctional prostheses control or also as input devices in human–computer interfaces. The complementary use of the open-access biomechanical simulation software, OpenSim, is demonstrated for the hand-movement classification performance visualization. A classifier was created from a previously captured database, which has 15 finger movements that were acquired during synchronized hand-movement repetitions with an 8-electrode sensor array placed on the forearm; a 92.89% recognition based on a complete movement was obtained. The OpenSim’s upper limb wrist model is employed, with movement in each of the joints of the hand–fingers. Several hand-motion visualizations were then generated, for the ideal hand movements, and for the best and the worst (53.03%) reproduction, to perceive the classification error in a specific task movement. This demonstrates the usefulness of this simulation tool before applying the classifier to a multifunctional prosthesis.

Context-dependent relationship in high-resolution micro-ECoG studies during finger movements

2020 ◽  
Vol 132 (5) ◽  
pp. 1358-1366
Author(s):  
Chao-Hung Kuo ◽  
Timothy M. Blakely ◽  
Jeremiah D. Wander ◽  
Devapratim Sarma ◽  
Jing Wu ◽  
...  
Keyword(s):  
High Resolution ◽  
Sensorimotor Cortex ◽  
Index Finger ◽  
Hand Movement ◽  
Cortical Activation ◽  
Hand Movements ◽  
Finger Movements ◽  
Thumb Movement ◽  
Neurosurgical Patients ◽  
The Relationship

OBJECTIVEThe activation of the sensorimotor cortex as measured by electrocorticographic (ECoG) signals has been correlated with contralateral hand movements in humans, as precisely as the level of individual digits. However, the relationship between individual and multiple synergistic finger movements and the neural signal as detected by ECoG has not been fully explored. The authors used intraoperative high-resolution micro-ECoG (µECoG) on the sensorimotor cortex to link neural signals to finger movements across several context-specific motor tasks.METHODSThree neurosurgical patients with cortical lesions over eloquent regions participated. During awake craniotomy, a sensorimotor cortex area of hand movement was localized by high-frequency responses measured by an 8 × 8 µECoG grid of 3-mm interelectrode spacing. Patients performed a flexion movement of the thumb or index finger, or a pinch movement of both, based on a visual cue. High-gamma (HG; 70–230 Hz) filtered µECoG was used to identify dominant electrodes associated with thumb and index movement. Hand movements were recorded by a dataglove simultaneously with µECoG recording.RESULTSIn all 3 patients, the electrodes controlling thumb and index finger movements were identifiable approximately 3–6-mm apart by the HG-filtered µECoG signal. For HG power of cortical activation measured with µECoG, the thumb and index signals in the pinch movement were similar to those observed during thumb-only and index-only movement, respectively (all p > 0.05). Index finger movements, measured by the dataglove joint angles, were similar in both the index-only and pinch movements (p > 0.05). However, despite similar activation across the conditions, markedly decreased thumb movement was observed in pinch relative to independent thumb-only movement (all p < 0.05).CONCLUSIONSHG-filtered µECoG signals effectively identify dominant regions associated with thumb and index finger movement. For pinch, the µECoG signal comprises a combination of the signals from individual thumb and index movements. However, while the relationship between the index finger joint angle and HG-filtered signal remains consistent between conditions, there is not a fixed relationship for thumb movement. Although the HG-filtered µECoG signal is similar in both thumb-only and pinch conditions, the actual thumb movement is markedly smaller in the pinch condition than in the thumb-only condition. This implies a nonlinear relationship between the cortical signal and the motor output for some, but importantly not all, movement types. This analysis provides insight into the tuning of the motor cortex toward specific types of motor behaviors.

scholarly journals Markovian analysis of cervical cell images.

1976 ◽  
Vol 24 (1) ◽  
pp. 138-144 ◽  
Author(s):  
N J Pressman
Keyword(s):  
Transition Probabilities ◽  
Classification Performance ◽  
Error Rates ◽  
Classification Error ◽  
Correct Classification ◽  
Cell Nuclei ◽  
Step Size ◽  
Cervical Cell ◽  
Texture Parameters ◽  
Markovian Analysis

Markovian analysis is a method to measure optical texture based on gray-level transition probabilities in digitized images. Experiments are described that investigate that classification performance of parameters generated by Markovian analysis. Results using Markov texture parameters show that the selection of a Markov step size strongly affects classification error rates and the number of parameters required to achieve the maximum correct classification rates. Markov texture parameters are shown to achieve high rates of correct classification in discriminating images of normal from abnormal cervical cell nuclei.

Abstract 343: Prediction of Lifesaving Interventions Using Changes in Vital Signs from Prehospital Scene to Emergency Department

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Jorge H Mena Munoz ◽  
Ashley Petersen ◽  
Francis X Guyette
Keyword(s):  
Emergency Department ◽  
Decision Trees ◽  
Recursive Partitioning ◽  
Vital Signs ◽  
Large Change ◽  
Predictive Performance ◽  
Classification Performance ◽  
Secondary Outcome ◽  
Classification Error ◽  
Trauma Patients

Objective: We investigate whether changes in vital signs between the prehospital scene and emergency department (ED) can be used to develop triage tools to predict the need for life-saving interventions (LSI) and survival in trauma patients. Methods: We analyzed a prospective cohort with any prehospital systolic blood pressure (SBP) ≤ 90 mmHg or Glasgow Coma Scale ≤ 8 who were admitted to an ED at 11 sites of the Resuscitation Outcomes Consortium. The primary outcome was the need for in-hospital LSI (e.g. invasive airway management, invasive bleeding control, blood transfusion, craniotomy, cardiopulmonary resuscitation). Secondary outcome was survival to hospital discharge. Changes in heart rate (HR), SBP, shock index (SI), and respiratory rate (RR) from first prehospital assessment to first ED assessment were considered as predictors in addition to sex, age, mechanism of injury, trauma center level, duration of transport, type of transport, and prehospital fluid volume. Decision trees for each outcome were developed using binary recursive partitioning with predictive performance measured using sensitivity, specificity, and classification error. Results: 5625 subjects were included in our analysis with 49% in need of LSI and 21% dying prior to discharge. Patients needing an LSI tended to either: (1) have an increasing SI (delta ≥ 0.22), (2) have a decreasing SI (delta < 0.22) and >500 mL prehospital fluids, or (3) have a decreasing SI (delta < 0.22), ≤500 mL prehospital fluids, and large change in RR (delta ≥ 9.5 or delta < -7.5). Those surviving to discharge tended to either: (1) have a decreasing SI (delta < 0.57) and a HR that did not decrease greatly (delta > -47) or (2) have an increase in SI (0.57 ≤ delta < 1) and a declining RR (delta < 5). LSI tree had a sensitivity of 58.7% and specificity of 63.3%. Survival tree had sensitivity of 96.2% and specificity of 21.3%. Conclusion: Though the decision trees were constructed with the best data in terms of initial triage and early secondary triage, the classification performance was limited. This highlights the difficulties of developing vital sign based triage tools to predict the need for LSI and survival.

scholarly journals Design and Optimization of Levenberg-Marquardt based Neural Network Classifier for EMG Signals to Identify Hand Motions

2013 ◽  
Vol 13 (3) ◽  
pp. 142-151 ◽  
Author(s):  
Muhammad Ibn Ibrahimy ◽  
Rezwanul Ahsan ◽  
Othman Omran Khalifa
Keyword(s):  
Neural Network ◽  
Single Channel ◽  
Learning Algorithms ◽  
Classification Performance ◽  
Training Algorithms ◽  
Neural Network Classifier ◽  
Hand Motion ◽  
Emg Signal ◽  
Levenberg Marquardt ◽  
Hidden Neurons

This paper presents an application of artificial neural network for the classification of single channel EMG signal in the context of hand motion detection. Seven statistical input features that are extracted from the preprocessed single channel EMG signals recorded for four predefined hand motions have been used for neural network classifier. Different structures of neural network, based on the number of hidden neurons and two prominent training algorithms, have been considered in the research to find out their applicability for EMG signal classification. The classification performances are analyzed for different architectures of neural network by considering the number of input features, number of hidden neurons, learning algorithms, correlation between network outputs and targets, and mean square error. Between the Levenberg-Marquardt and scaled conjugate gradient learning algorithms, the aforesaid algorithm shows better classification performance. The outcomes of the research show that the optimal design of Levenberg-Marquardt based neural network classifier can perform well with an average classification success rate of 88.4%. A comparison of results has also been presented to validate the effectiveness of the designed neural network classifier to discriminate EMG signals.

scholarly journals Typical somatomotor physiology of the hand is preserved in a patient with an amputated arm

2021 ◽  
Author(s):  
Max van den Boom ◽  
Kai J. Miller ◽  
Nicholas M. Gregg ◽  
Gabriela Ojeda ◽  
Kendall H. Lee ◽  
...  
Keyword(s):  
Frequency Band ◽  
Sensorimotor Cortex ◽  
Hand Movement ◽  
Phantom Limb Pain ◽  
Phantom Limb ◽  
Support Vector ◽  
Finger Movements ◽  
Prosthetic Devices ◽  
Neural Prosthetic ◽  
Brainstem Stroke

AbstractElectrophysiological signals in the human motor system may change in different ways after deafferentation, with some studies emphasizing reorganization while others propose retained physiology. Understanding whether motor electrophysiology is retained over longer periods of time can be invaluable for patients with paralysis (e.g. ALS or brainstem stroke) when signals from sensorimotor areas may be used for communication or control over neural prosthetic devices. In addition, a maintained electrophysiology can potentially benefit the treatment of phantom limb pains through prolonged use of these signals in a brain-machine interface (BCI).Here, we were presented with the unique opportunity to investigate the physiology of the sensorimotor cortex in a patient with an amputated arm using electrocorticographic (ECoG) measurements. While implanted with an ECoG grid for clinical evaluation of electrical stimulation for phantom limb pain, the patient performed attempted finger movements with the contralateral (lost) hand and executed finger movements with the ipsilateral (healthy) hand.The electrophysiology of the sensorimotor cortex contralateral to the amputated hand remained very similar to that of hand movement in healthy people, with a spatially focused increase of high-frequency band (65-175Hz; HFB) power over the hand region and a distributed decrease in low-frequency band (15-28Hz; LFB) power. The representation of the three different fingers (thumb, index and little) remained intact and HFB patterns could be decoded using support vector learning at single-trial classification accuracies of >90%, based on the first 1-3s of the HFB response. These results demonstrate that hand representations are largely retained in the motor cortex. The intact physiological response of the amputated hand, the high distinguishability of the fingers and fast temporal peak are encouraging for neural prosthetic devices that target the sensorimotor cortex.

Dynamic Response of Human Legs due to Horizontal Jump

2012 ◽  
Author(s):  
Srikanth Ravuri ◽  
Fred Barez ◽  
David Wagner ◽  
Jim Kao
Keyword(s):  
Reaction Force ◽  
Three Dimensional ◽  
Simulation Software ◽  
Leap Motion ◽  
Biomechanical Simulation ◽  
Maximum Reaction ◽  
Internal Joint ◽  
Joint Reaction Forces ◽  
Reaction Forces ◽  
Distal Direction

Jumping is a coordinated extension of the human body through combined strength and agility to perform a leap motion far enough for the feet to land on the ground. However, the repeated reaction forces and the resulting stresses on the ankle, knee and hip joints may cause injuries to a person. A primary mechanism of such injuries is suggested to be the acute high impact loads experienced during the landing in a horizontal jump. The goal of this study is to determine the reaction force distribution at the joints in the lower extremities during the horizontal jump. A detailed biomechanical system was constructed to calculate the reaction forces generated during the horizontal jump. The horizontal jump kinematics of a participant was measured using a three-dimensional motion capture system and the landing forces were measured using two force plates. Biomechanical simulation software was used to calculate the internal joint reaction forces at the ankle, knee, and hip. It was determined that the maximum reaction forces primarily occurred in the proximo/distal direction of the hip, 2,300 N; and ankle, 2,700 N. However, at the knee joint, the maximum reaction force was determined to be in antero/posterior direction, at 2,000 N; and proximo/distal direction, at 2,100 N, respectively.

Activation of Distinct Motor Cortex Regions During Ipsilateral and Contralateral Finger Movements

1999 ◽  
Vol 81 (1) ◽  
pp. 383-387 ◽  
Author(s):  
Steven C. Cramer ◽  
Seth P. Finklestein ◽  
Judith D. Schaechter ◽  
George Bush ◽  
Bruce R. Rosen
Keyword(s):  
Motor Cortex ◽  
Index Finger ◽  
Hand Movement ◽  
Stroke Recovery ◽  
Finger Tapping ◽  
Hand Movements ◽  
Precentral Gyrus ◽  
Finger Movements ◽  
Stroke Patients ◽  
Contralateral Hand

Cramer, Steven C., Seth P. Finklestein, Judith D. Schaechter, George Bush, and Bruce R. Rosen. Activation of distinct motor cortex regions during ipsilateral and contralateral finger movements. J. Neurophysiol. 81: 383–387, 1999. Previous studies have shown that unilateral finger movements are normally accompanied by a small activation in ipsilateral motor cortex. The magnitude of this activation has been shown to be altered in a number of conditions, particularly in association with stroke recovery. The site of this activation, however, has received limited attention. To address this question, functional magnetic resonance imaging (MRI) was used to study precentral gyrus activation in six control and three stroke patients during right index finger tapping, then during left index finger tapping. In each hemisphere, the most significantly activated site ( P < 0.001 required) was identified during ipsilateral and during contralateral finger tapping. In the motor cortex of each hemisphere, the site activated during use of the ipsilateral hand differed from that found during use of the contralateral hand. Among the 11 control hemispheres showing significant activation during both motor tasks, the site for ipsilateral hand representation (relative to contralateral hand site in the same hemisphere) was significantly shifted ventrally in all 11 hemispheres (mean, 11 mm), laterally in 10/11 hemispheres (mean, 12 mm), and anteriorly in 8/11 hemispheres (mean, 10 mm). In 6 of 11 hemispheres, tapping of the contralateral finger simultaneously activated both the ipsilateral and the contralateral finger sites, suggesting bilateral motor control by the ipsilateral finger site. The sites activated during ipsilateral and contralateral hand movement showed similar differences in the unaffected hemisphere of stroke patients. The region of motor cortex activated during ipsilateral hand movements is spatially distinct from that identified during contralateral hand movements.

scholarly journals Hand Movement Classification Using Burg Reflection Coefficients

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 475 ◽  
Author(s):  
Daniel Ramírez-Martínez ◽  
Mariel Alfaro-Ponce ◽  
Oleksiy Pogrebnyak ◽  
Mario Aldape-Pérez ◽  
Amadeo-José Argüelles-Cruz
Keyword(s):  
Feature Extraction ◽  
Time Domain ◽  
Hand Movement ◽  
Classification Performance ◽  
Reflection Coefficients ◽  
Classification Rate ◽  
Prosthetic Devices ◽  
Wide Range ◽  
Preprocessing Technique ◽  
Electromyographic Signals

Classification of electromyographic signals has a wide range of applications, from clinical diagnosis of different muscular diseases to biomedical engineering, where their use as input for the control of prosthetic devices has become a hot topic of research. The challenge of classifying these signals relies on the accuracy of the proposed algorithm and the possibility of its implementation in hardware. This paper considers the problem of electromyography signal classification, solved with the proposed signal processing and feature extraction stages, with the focus lying on the signal model and time domain characteristics for better classification accuracy. The proposal considers a simple preprocessing technique that produces signals suitable for feature extraction and the Burg reflection coefficients to form learning and classification patterns. These coefficients yield a competitive classification rate compared to the time domain features used. Sometimes, the feature extraction from electromyographic signals has shown that the procedure can omit less useful traits for machine learning models. Using feature selection algorithms provides a higher classification performance with as few traits as possible. The algorithms achieved a high classification rate up to 100% with low pattern dimensionality, with other kinds of uncorrelated attributes for hand movement identification.

Automatic Tonnage Monitoring for Missing Part Detection in Multi-Operation Forging Processes

2010 ◽  
Vol 132 (5) ◽  
Author(s):  
Yong Lei ◽  
Zhisheng Zhang ◽  
Jionghua Jin
Keyword(s):  
Continuous Production ◽  
Hierarchical Classification ◽  
Principal Component ◽  
Classification Performance ◽  
Classification Error ◽  
Signal Segmentation ◽  
Forging Process ◽  
Operation Process ◽  
Classifier Performance ◽  
New Feature

In multi-operation forging processes, the process fault due to missing parts from dies is a critical concern. The objective of this paper is to develop an effective method for detecting missing parts by using automatic classification of tonnage signals during continuous production. In this paper, a new feature selection and hierarchical classification method is developed to improve the classification performance for multiclass faults. In the development of the methodology, the signal segmentation is conducted at the first step based on an offline station-by-station test in a forging process. Afterwards, the principal component analysis is conducted on the segmented tonnage signals to generate the principal component (PC) features to be selected for designing the classifier. Finally, the optimal selection of PC features is integrated with the design of a hierarchical classifier by using the criterion of minimizing the probabilities of misclassification among classes. A case study using a real-world forging process is provided in the paper, which demonstrates the effectiveness of the developed methodology for detecting and diagnosing the missing parts faults in the multiple forging operation process. The classifier performance is also validated through the cross-validations to achieve a given average classification error.

scholarly journals FMG- and RNN-Based Estimation of Motor Intention of Upper-Limb Motion in Human-Robot Collaboration

2020 ◽  
Vol 7 ◽  
Author(s):  
Mohammad Anvaripour ◽  
Mahta Khoshnam ◽  
Carlo Menon ◽  
Mehrdad Saif
Keyword(s):  
Industrial Robot ◽  
Hand Movement ◽  
Movement Pattern ◽  
Machine Learning Algorithms ◽  
Hand Motion ◽  
Course Of Action ◽  
Motor Intention ◽  
Interaction Scheme ◽  
Force Myography ◽  
Human Robot Collaboration

Research on human-robot interactions has been driven by the increasing employment of robotic manipulators in manufacturing and production. Toward developing more effective human-robot collaboration during shared tasks, this paper proposes an interaction scheme by employing machine learning algorithms to interpret biosignals acquired from the human user and accordingly planning the robot reaction. More specifically, a force myography (FMG) band was wrapped around the user's forearm and was used to collect information about muscle contractions during a set of collaborative tasks between the user and an industrial robot. A recurrent neural network model was trained to estimate the user's hand movement pattern based on the collected FMG data to determine whether the performed motion was random or intended as part of the predefined collaborative tasks. Experimental evaluation during two practical collaboration scenarios demonstrated that the trained model could successfully estimate the category of hand motion, i.e., intended or random, such that the robot either assisted with performing the task or changed its course of action to avoid collision. Furthermore, proximity sensors were mounted on the robotic arm to investigate if monitoring the distance between the user and the robot had an effect on the outcome of the collaborative effort. While further investigation is required to rigorously establish the safety of the human worker, this study demonstrates the potential of FMG-based wearable technologies to enhance human-robot collaboration in industrial settings.

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