Offline Repeatability Correlates with Real-Time Performance of Pattern Recognition Controllers

2021 ◽  
pp. 691-696
Author(s):  
Yuni Teh ◽  
Levi J. Hargrove
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Time Performance

scholarly journals Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5677
Author(s):  
Sara Abbaspour ◽  
Autumn Naber ◽  
Max Ortiz-Catalan ◽  
Hamid GholamHosseini ◽  
Maria Lindén
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Classification Accuracy ◽  
Likelihood Estimation ◽  
The Other ◽  
Time Data ◽  
Linear Discriminant ◽  
Time Performance ◽  
Performance Metric ◽  
Upper Limb Prostheses

Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200–300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively.

scholarly journals Real-time and offline evaluation of myoelectric pattern recognition for the decoding of hand movements

2020 ◽  
Author(s):  
Sara Abbaspour ◽  
Autumn Naber ◽  
Max Ortiz-Catalan ◽  
Hamid Gholamhosseini ◽  
Maria Lindén
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Classification Accuracy ◽  
Likelihood Estimation ◽  
Linear Discriminant ◽  
Time Performance ◽  
Performance Metric ◽  
Recognition Algorithms ◽  
Upper Limb Prostheses ◽  
Electromyographic Signals

<p><a></a><a>Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. Recent literature has underscored differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Since the majority of investigations on pattern recognition algorithms have been conducted offline by performing the analysis on pre-recorded datasets, less knowledge has been gained with respect to real-time performance (i.e., classification when new data becomes available with limits on latency under 200-300 milliseconds). </a>Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from the dominant forearm of fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that Linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p<0.05) outperformed other classifiers with an average classification accuracy of above 97%. The real-time investigation revealed that in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms in classification accuracy (above 68%) and completion rate (above 69%).</p>

scholarly journals Real-time and offline evaluation of myoelectric pattern recognition for the decoding of hand movements

2020 ◽  
Author(s):  
Sara Abbaspour ◽  
Autumn Naber ◽  
Max Ortiz-Catalan ◽  
Hamid Gholamhosseini ◽  
Maria Lindén
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Classification Accuracy ◽  
Likelihood Estimation ◽  
Linear Discriminant ◽  
Time Performance ◽  
Performance Metric ◽  
Recognition Algorithms ◽  
Upper Limb Prostheses ◽  
Electromyographic Signals

<p><a></a><a>Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. Recent literature has underscored differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Since the majority of investigations on pattern recognition algorithms have been conducted offline by performing the analysis on pre-recorded datasets, less knowledge has been gained with respect to real-time performance (i.e., classification when new data becomes available with limits on latency under 200-300 milliseconds). </a>Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from the dominant forearm of fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that Linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p<0.05) outperformed other classifiers with an average classification accuracy of above 97%. The real-time investigation revealed that in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms in classification accuracy (above 68%) and completion rate (above 69%).</p>

scholarly journals Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yanjuan Geng ◽  
Oluwarotimi Williams Samuel ◽  
Yue Wei ◽  
Guanglin Li
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Clinical Implementation ◽  
Real Time Control ◽  
Test Environment ◽  
The Real ◽  
Time Performance ◽  
Arm Position ◽  
Time Control ◽  
The Impact

Previous studies have showed that arm position variations would significantly degrade the classification performance of myoelectric pattern-recognition-based prosthetic control, and the cascade classifier (CC) and multiposition classifier (MPC) have been proposed to minimize such degradation in offline scenarios. However, it remains unknown whether these proposed approaches could also perform well in the clinical use of a multifunctional prosthesis control. In this study, the online effect of arm position variation on motion identification was evaluated by using a motion-test environment (MTE) developed to mimic the real-time control of myoelectric prostheses. The performance of different classifier configurations in reducing the impact of arm position variation was investigated using four real-time metrics based on dataset obtained from transradial amputees. The results of this study showed that, compared to the commonly used motion classification method, the CC and MPC configurations improved the real-time performance across seven classes of movements in five different arm positions (8.7% and 12.7% increments of motion completion rate, resp.). The results also indicated that high offline classification accuracy might not ensure good real-time performance under variable arm positions, which necessitated the investigation of the real-time control performance to gain proper insight on the clinical implementation of EMG-pattern-recognition-based controllers for limb amputees.

Real-time Performance Monitoring of MPC Based on Covariance Index Prediction

2014 ◽  
Vol 39 (5) ◽  
pp. 658-663 ◽  
Author(s):  
Xue-Min TIAN ◽  
Ya-Jie SHI ◽  
Yu-Ping CAO
Keyword(s):  
Real Time ◽  
Performance Monitoring ◽  
Time Performance
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