scholarly journals Concurrent surface electromyography and force myography classification during times of prosthetic socket shift and user fatigue

2017 ◽  
Vol 4 ◽  
pp. 205566831770873 ◽  
Author(s):  
Joe Sanford ◽  
Rita Patterson ◽  
Dan O Popa
Keyword(s):  
Surface Electromyography ◽  
Muscle Activation ◽  
Surface Emg ◽  
Training Data ◽  
Control Input ◽  
Prosthetic Devices ◽  
Prosthetic Socket ◽  
User Fatigue ◽  
Force Myography

Objective Surface electromyography has been a long-standing source of signals for control of powered prosthetic devices. By contrast, force myography is a more recent alternative to surface electromyography that has the potential to enhance reliability and avoid operational challenges of surface electromyography during use. In this paper, we report on experiments conducted to assess improvements in classification of surface electromyography signals through the addition of collocated force myography consisting of piezo-resistive sensors. Methods Force sensors detect intrasocket pressure changes upon muscle activation due to changes in muscle volume during activities of daily living. A heterogeneous sensor configuration with four surface electromyography–force myography pairs was investigated as a control input for a powered upper limb prosthetic. Training of two different multilevel neural perceptron networks was employed during classification and trained on data gathered during experiments simulating socket shift and muscle fatigue. Results Results indicate that intrasocket pressure data used in conjunction with surface EMG data can improve classification of human intent and control of a powered prosthetic device compared to traditional, surface electromyography only systems. Significance Additional sensors lead to significantly better signal classification during times of user fatigue, poor socket fit, as well as radial and ulnar wrist deviation. Results from experimentally obtained training data sets are presented.

MYOELECTRIC ACTIVATION DIFFERENCES IN TRANSFEMORAL AMPUTEES DURING LOCOMOTOR STATE TRANSITIONS

2016 ◽  
Vol 28 (06) ◽  
pp. 1650041 ◽  
Author(s):  
Bryson H. Nakamura ◽  
Michael E. Hahn
Keyword(s):  
Muscle Activation ◽  
Vastus Lateralis ◽  
Statistical Parametric Mapping ◽  
State Transitions ◽  
Prosthetic Devices ◽  
Lower Extremity Muscle ◽  
Transfemoral Amputees ◽  
Recruitment Patterns ◽  
The Individual

Locomotor state transitions are challenging for transfemoral (TF) amputees due to the lack of active knee control even in the current powered prosthetic devices. Myoelectric activation has been used successfully to classify steady-state locomotion states, but classification of transitions between locomotion states remains a challenge, especially for TF amputees. The purpose of this study was to determine if lower-extremity muscle activation differences between pre-transition and transition gait cycles occur in the involved or uninvolved limb of TF amputees during locomotion state transitions. Surface electromyography (EMG) was collected from residual muscles on the involved limb and from the uninvolved limb from five TF amputees as they transitioned between different locomotion states (level ground, ramp ascent/descent, stair ascent/descent). Statistical parametric mapping (SPM) was used to assess differences in activation. When analyzed as a group, the only significant differences were observed in the vastus lateralis of the uninvolved limb. High inter-subject variation reduced the significance of other pattern differences. Further inspection revealed that the individual subjects expressed three different recruitment patterns. These recruitment patterns may indicate compensatory strategies adopted by the subjects over the years since amputation. Furthermore, the separate recruitment patterns suggest the need for individualized locomotion transition classification algorithms rather than a general classification scheme.

scholarly journals Ictal quantitative surface electromyography correlates with postictal EEG suppression

Neurology ◽  
2020 ◽  
Vol 94 (24) ◽  
pp. e2567-e2576 ◽  
Author(s):  
Anca A. Arbune ◽  
Isa Conradsen ◽  
Damon P. Cardenas ◽  
Luke E. Whitmire ◽  
Shannon R. Voyles ◽  
...  
Keyword(s):  
Muscle Activation ◽  
Silent Period ◽  
Deltoid Muscle ◽  
Surface Emg ◽  
Stepwise Multiple Regression ◽  
Unexpected Death ◽  
Increased Risk ◽  
Convulsive Seizures

ObjectiveTo test the hypothesis that neurophysiologic biomarkers of muscle activation during convulsive seizures reveal seizure severity and to determine whether automatically computed surface EMG parameters during seizures can predict postictal generalized EEG suppression (PGES), indicating increased risk for sudden unexpected death in epilepsy. Wearable EMG devices have been clinically validated for automated detection of generalized tonic-clonic seizures. Our goal was to use quantitative EMG measurements for seizure characterization and risk assessment.MethodsQuantitative parameters were computed from surface EMGs recorded during convulsive seizures from deltoid and brachial biceps muscles in patients admitted to long-term video-EEG monitoring. Parameters evaluated were the durations of the seizure phases (tonic, clonic), durations of the clonic bursts and silent periods, and the dynamics of their evolution (slope). We compared them with the duration of the PGES.ResultsWe found significant correlations between quantitative surface EMG parameters and the duration of PGES (p < 0.001). Stepwise multiple regression analysis identified as independent predictors in deltoid muscle the duration of the clonic phase and in biceps muscle the duration of the tonic-clonic phases, the average silent period, and the slopes of the silent period and clonic bursts. The surface EMG-based algorithm identified seizures at increased risk (PGES ≥20 seconds) with an accuracy of 85%.ConclusionsIctal quantitative surface EMG parameters correlate with PGES and may identify seizures at high risk.Classification of evidenceThis study provides Class II evidence that during convulsive seizures, surface EMG parameters are associated with prolonged postictal generalized EEG suppression.

scholarly journals Classification of Walking Environments Using Deep Learning Approach Based on Surface EMG Sensors Only

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4204
Author(s):  
Pankwon Kim ◽  
Jinkyu Lee ◽  
Choongsoo S. Shin
Keyword(s):  
Muscle Activation ◽  
Lower Extremities ◽  
Surface Emg ◽  
High Accuracy ◽  
Triceps Surae ◽  
Assistive Device ◽  
Artificial Neural Network Ann ◽  
Walking Environment ◽  
Semg Signals

Classification of terrain is a vital component in giving suitable control to a walking assistive device for the various walking conditions. Although surface electromyography (sEMG) signals have been combined with inputs from other sensors to detect walking intention, no study has yet classified walking environments using sEMG only. Therefore, the purpose of this study is to classify the current walking environment based on the entire sEMG profile gathered from selected muscles in the lower extremities. The muscle activations of selected muscles in the lower extremities were measured in 27 participants while they walked over flat-ground, upstairs, downstairs, uphill, and downhill. An artificial neural network (ANN) was employed to classify these walking environments using the entire sEMG profile recorded for all muscles during the stance phase. The result shows that the ANN was able to classify the current walking environment with high accuracy of 96.3% when using activation from all muscles. When muscle activation from flexor/extensor groups in the knee, ankle, and metatarsophalangeal joints were used individually to classify the environment, the triceps surae muscle activation showed the highest classification accuracy of 88.9%. In conclusion, a current walking environment was classified with high accuracy using an ANN based on only sEMG signals.

scholarly journals Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors

2021 ◽  
Vol 15 ◽  
Author(s):  
Jianting Fu ◽  
Shizhou Cao ◽  
Linqin Cai ◽  
Lechan Yang
Keyword(s):  
High Precision ◽  
Gesture Recognition ◽  
Surface Electromyography ◽  
Recognition Accuracy ◽  
Short Circuit ◽  
Sampling Rate ◽  
Surface Emg ◽  
Artificial Limb ◽  
The Time Domain

Finger gesture recognition (FGR) plays a crucial role in achieving, for example, artificial limb control and human-computer interaction. Currently, the most common methods of FGR are visual-based, voice-based, and surface electromyography (EMG)-based ones. Among them, surface EMG-based FGR is very popular and successful because surface EMG is a cumulative bioelectric signal from the surface of the skin that can accurately and intuitively represent the force of the fingers. However, existing surface EMG-based methods still cannot fully satisfy the required recognition accuracy for artificial limb control as the lack of high-precision sensor and high-accurate recognition model. To address this issue, this study proposes a novel FGR model that consists of sensing and classification of surface EMG signals (SC-FGR). In the proposed SC-FGR model, wireless sensors with high-precision surface EMG are first developed for acquiring multichannel surface EMG signals from the forearm. Its resolution is 16 Bits, the sampling rate is 2 kHz, the common-mode rejection ratio (CMRR) is less than 70 dB, and the short-circuit noise (SCN) is less than 1.5 μV. In addition, a convolution neural network (CNN)-based classification algorithm is proposed to achieve FGR based on acquired surface EMG signals. The CNN is trained on a spectrum map transformed from the time-domain surface EMG by continuous wavelet transform (CWT). To evaluate the proposed SC-FGR model, we compared it with seven state-of-the-art models. The experimental results demonstrate that SC-FGR achieves 97.5% recognition accuracy on eight kinds of finger gestures with five subjects, which is much higher than that of comparable models.

scholarly journals NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2503
Author(s):  
Taro Suzuki ◽  
Yoshiharu Amano
Keyword(s):  
Machine Learning ◽  
Satellite System ◽  
Training Data ◽  
Support Vector ◽  
Positioning Errors ◽  
Automated Method ◽  
Global Navigation Satellite ◽  
Better Than ◽  
Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

scholarly journals Artificial Intelligence, 3D Documentation, and Rock Art—Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images

2021 ◽  
Author(s):  
Christian Horn ◽  
Oscar Ivarsson ◽  
Cecilia Lindhé ◽  
Rich Potter ◽  
Ashely Green ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bronze Age ◽  
Data Augmentation ◽  
Rock Art ◽  
Training Data ◽  
Future Research ◽  
Rock Surface ◽  
Southern Scandinavia ◽  
Bounding Boxes

AbstractRock art carvings, which are best described as petroglyphs, were produced by removing parts of the rock surface to create a negative relief. This tradition was particularly strong during the Nordic Bronze Age (1700–550 BC) in southern Scandinavia with over 20,000 boats and thousands of humans, animals, wagons, etc. This vivid and highly engaging material provides quantitative data of high potential to understand Bronze Age social structures and ideologies. The ability to provide the technically best possible documentation and to automate identification and classification of images would help to take full advantage of the research potential of petroglyphs in southern Scandinavia and elsewhere. We, therefore, attempted to train a model that locates and classifies image objects using faster region-based convolutional neural network (Faster-RCNN) based on data produced by a novel method to improve visualizing the content of 3D documentations. A newly created layer of 3D rock art documentation provides the best data currently available and has reduced inscribed bias compared to older methods. Several models were trained based on input images annotated with bounding boxes produced with different parameters to find the best solution. The data included 4305 individual images in 408 scans of rock art sites. To enhance the models and enrich the training data, we used data augmentation and transfer learning. The successful models perform exceptionally well on boats and circles, as well as with human figures and wheels. This work was an interdisciplinary undertaking which led to important reflections about archaeology, digital humanities, and artificial intelligence. The reflections and the success represented by the trained models open novel avenues for future research on rock art.

scholarly journals Antagonist Muscle Co-Activation during Kettlebell Single Arm Swing Exercise

2021 ◽  
Vol 11 (9) ◽  
pp. 4033
Author(s):  
Ahmed Salem ◽  
Amr Hassan ◽  
Markus Tilp ◽  
Abdel-Rahman Akl
Keyword(s):  
Muscle Activation ◽  
Surface Emg ◽  
Antagonist Muscle ◽  
Arm Swing ◽  
Shoulder Muscles ◽  
Antagonist Muscles ◽  
Co Activation ◽  
Integrated Emg ◽  
Post Hoc ◽  
Electrical Muscle

The purpose of this study was to determine the muscle activation and co-activation of selected muscles during the kettlebell single arm swing exercise. To the best of our knowledge, this is the first study investigating the muscle co-activation of a kettlebell single arm swing exercise. Nine volunteers participated in the present study (age: 22.6 ± 3.8 years; body mass: 80.4 ± 9.2 kg; height: 175.6 ± 7.5 cm). The electrical muscle activity of eight right agonist/antagonist muscles (AD/PD, ESL/RA, ESI/EO, and GM/RF) were recorded using a surface EMG system (Myon m320RX; Myon, Switzerland) and processed using the integrated EMG to calculate a co-activation index (CoI) for the ascending and descending phases. A significant effect of the ascending and descending phases on the muscles’ CoI was observed. Post hoc analyses showed that the co-activation was significantly higher in the descending phase compared to that in the ascending phase of AD/PD CoI (34.25 ± 18.03% and 24.75 ± 13.03%, p < 0.001), ESL/RA CoI (34.97 ± 17.86% and 24.19 ± 10.32%, p < 0.001), ESI/EO CoI (41.14 ± 10.72% and 30.87 ± 11.26%, p < 0.001), and GM/RF CoI (27.49 ± 12.97% and 34.98 ± 14.97%, p < 0.001). In conclusion, the co-activation of the shoulder muscles varies within the kettlebell single arm swing. The highest level of co-activation was observed in the descending phase of AD/PD and GM/RF CoI, and the lowest level of co-activation was observed during the descending phase, ESL/RA and ESI/EO CoI. In addition, the highest level of co-activation was observed in the ascending phase of ESL/RA and ESI/EO CoI, and the lowest level of co-activation was observed during the ascending phase, AD/PD and GM/RF CoI. The co-activation index could be a useful method for the interpretation of the shoulder and core muscles’ co-activity during a kettlebell single arm swing.

Deep Learning based Tomato’s Ripe and Unripe Classification System

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Ccd Camera ◽  
Agricultural Products ◽  
Training Data ◽  
Maturity Level ◽  
Agriculture Sector ◽  
The Past ◽  
State Of Art

Effective productivity estimates of fresh produced crops are very essential for efficient farming, commercial planning, and logistical support. In the past ten years, machine learning (ML) algorithms have been widely used for grading and classification of agricultural products in agriculture sector. However, the precise and accurate assessment of the maturity level of tomatoes using ML algorithms is still a quite challenging to achieve due to these algorithms being reliant on hand crafted features. Hence, in this paper we propose a deep learning based tomato maturity grading system that helps to increase the accuracy and adaptability of maturity grading tasks with less amount of training data. The performance of proposed system is assessed on the real tomato datasets collected from the open fields using Nikon D3500 CCD camera. The proposed approach achieved an average maturity classification accuracy of 99.8 % which seems to be quite promising in comparison to the other state of art methods.

Initial phase of maximal voluntary and electrically stimulated knee extension torque development at different knee angles

2004 ◽  
Vol 97 (5) ◽  
pp. 1693-1701 ◽  
Author(s):  
C. J. de Ruiter ◽  
R. D. Kooistra ◽  
M. I. Paalman ◽  
A. de Haan
Keyword(s):  
Muscle Activation ◽  
Knee Extensor ◽  
Surface Emg ◽  
Knee Extension ◽  
Voluntary Contraction ◽  
Voluntary Activation ◽  
Knee Angle ◽  
Time Integral ◽  
Voluntary Contractions ◽  
Torque Development

We investigated the capacity for torque development and muscle activation at the onset of fast voluntary isometric knee extensions at 30, 60, and 90° knee angle. Experiments were performed in subjects ( n = 7) who had high levels (>90%) of activation at the plateau of maximal voluntary contractions. During maximal electrical nerve stimulation (8 pulses at 300 Hz), the maximal rate of torque development (MRTD) and torque time integral over the first 40 ms (TTI40) changed in proportion with torque at the different knee angles (highest values at 60°). At each knee angle, voluntary MRTD and stimulated MRTD were similar ( P < 0.05), but time to voluntary MRTD was significantly longer. Voluntary TTI40 was independent ( P > 0.05) of knee angle and on average (all subjects and angles) only 40% of stimulated TTI40. However, among subjects, the averaged (across knee angles) values ranged from 10.3 ± 3.1 to 83.3 ± 3.2% and were positively related ( r2 = 0.75, P < 0.05) to the knee-extensor surface EMG at the start of torque development. It was concluded that, although all subjects had high levels of voluntary activation at the plateau of maximal voluntary contraction, among subjects and independent of knee angle, the capacity for fast muscle activation varied substantially. Moreover, in all subjects, torque developed considerably faster during maximal electrical stimulation than during maximal voluntary effort. At different knee angles, stimulated MRTD and TTI40 changed in proportion with stimulated torque, but voluntary MRTD and TTI40 changed less than maximal voluntary torque.

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