A real time performance assessment of simultaneous pattern recognition control for multi-functional upper limb prostheses

2013 ◽  
Author(s):  
Sophie M. Wurth ◽  
Levi J. Hargrove
Keyword(s):  
Pattern Recognition ◽  
Performance Assessment ◽  
Real Time ◽  
Upper Limb ◽  
Time Performance ◽  
Upper Limb Prostheses

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 Users’ and therapists’ perceptions of myoelectric multi-function upper limb prostheses with conventional and pattern recognition control

PLoS ONE ◽  
2019 ◽  
Vol 14 (8) ◽  
pp. e0220899 ◽  
Author(s):  
Andreas W. Franzke ◽  
Morten B. Kristoffersen ◽  
Raoul M. Bongers ◽  
Alessio Murgia ◽  
Barbara Pobatschnig ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Upper Limb ◽  
Upper Limb Prostheses

scholarly journals Adaptive Windowing Framework for Surface Electromyogram-Based Pattern Recognition System for Transradial Amputees

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2402 ◽  
Author(s):  
Ali Al-Timemy ◽  
Guido Bugmann ◽  
Javier Escudero
Keyword(s):  
Pattern Recognition ◽  
Upper Limb ◽  
Time Domain ◽  
Window Size ◽  
Error Rates ◽  
Classification Error ◽  
Linear Discriminant ◽  
Emg Signal ◽  
Upper Limb Prosthesis ◽  
Upper Limb Prostheses

Electromyogram (EMG)-based Pattern Recognition (PR) systems for upper-limb prosthesis control provide promising ways to enable an intuitive control of the prostheses with multiple degrees of freedom and fast reaction times. However, the lack of robustness of the PR systems may limit their usability. In this paper, a novel adaptive time windowing framework is proposed to enhance the performance of the PR systems by focusing on their windowing and classification steps. The proposed framework estimates the output probabilities of each class and outputs a movement only if a decision with a probability above a certain threshold is achieved. Otherwise (i.e., all probability values are below the threshold), the window size of the EMG signal increases. We demonstrate our framework utilizing EMG datasets collected from nine transradial amputees who performed nine movement classes with Time Domain Power Spectral Descriptors (TD-PSD), Wavelet and Time Domain (TD) feature extraction (FE) methods and a Linear Discriminant Analysis (LDA) classifier. Nonetheless, the concept can be applied to other types of features and classifiers. In addition, the proposed framework is validated with different movement and EMG channel combinations. The results indicate that the proposed framework works well with different FE methods and movement/channel combinations with classification error rates of approximately 13% with TD-PSD FE. Thus, we expect our proposed framework to be a straightforward, yet important, step towards the improvement of the control methods for upper-limb prostheses.

ElectroMyoGram Pattern Recognition for Real-Time Control of Upper Limb Prosthesis

2016 ◽  
Vol 6 (8) ◽  
pp. 1872-1880 ◽  
Author(s):  
Enas Abdulhay ◽  
Ruba Khnouf ◽  
Abeer Bakeir ◽  
Razan Al-Asasfeh ◽  
Heba Khader
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Upper Limb ◽  
Real Time Control ◽  
Time Control ◽  
Upper Limb Prosthesis ◽  
Limb Prosthesis

scholarly journals Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4596 ◽  
Author(s):  
Nawadita Parajuli ◽  
Neethu Sreenivasan ◽  
Paolo Bifulco ◽  
Mario Cesarelli ◽  
Sergio Savino ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Upper Limb ◽  
Real World ◽  
Healthcare Sector ◽  
Limb Amputation ◽  
Future Research ◽  
Myoelectric Prosthesis ◽  
Upper Limb Prosthesis ◽  
Maximum Utility

Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations.

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