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.

scholarly journals Smartphone supported upper limb prosthesis

2015 ◽  
Vol 1 (1) ◽  
pp. 484-487
Author(s):  
D. Hepp ◽  
J. Kirsch ◽  
F. Capanni
Keyword(s):  
Neural Network ◽  
Upper Limb ◽  
Design Development ◽  
Test Setup ◽  
Software Application ◽  
Patient Feedback ◽  
Emg Signal ◽  
Upper Limb Prosthesis ◽  
Upper Limb Prostheses ◽  
Limb Prosthesis

AbstractState of the art upper limb prostheses offer up to six active DoFs (degrees of freedom) and are controlled using different grip patterns. This low number of DoFs combined with a machine-human-interface which does not provide control over all DoFs separately result in a lack of usability for the patient. The aim of this novel upper limb prosthesis is both offering simplified control possibilities for changing grip patterns depending on the patients’ priorities and the improvement of grasp capability. Design development followed the design process requirements given by the European Medical Device Directive 93/42 ECC and was structured into the topics mechanics, software and drive technology. First user needs were identified by literature research and by patient feedback. Consequently, concepts were evaluated against technical and usability requirements. A first evaluation prototype with one active DoF per finger was manufactured. In a second step a test setup with two active DoF per finger was designed. The prototype is connected to an Android based smartphone application. Two main grip patterns can be preselected in the software application and afterwards changed and used by the EMG signal. Three different control algorithms can be selected: “all-day”, “fine” and “tired muscle”. Further parameters can be adjusted to customize the prosthesis to the patients’ needs. First patient feedback certified the prosthesis an improved level of handling compared to the existing devices. Using the two DoF test setup, the possibilities of finger control with a neural network are evaluated at the moment. In a first user feedback test, the smartphone based software application increased the device usability, e.g. the change within preselected grip patterns and the “tired muscle” algorithm. Although the overall software application was positively rated, the handling of the prosthesis itself needs to be proven within a patient study to be performed next. The capability of the neural network to control the hand has also to be proven in a next step.

INTELLIGENT UPPER-LIMB PROSTHETIC CONTROL (iULP) WITH NOVEL FEATURE EXTRACTION METHOD FOR PATTERN RECOGNITION USING EMG

2021 ◽  
pp. 2150043
Author(s):  
SIDHARTH PANCHOLI ◽  
AMIT M. JOSHI
Keyword(s):  
Pattern Recognition ◽  
Upper Limb ◽  
Classification Accuracy ◽  
Time Derivative ◽  
Machine Learning Algorithms ◽  
Prosthetic Control ◽  
Myoelectric Prosthesis ◽  
Feature Extraction Method ◽  
Similar Work ◽  
Emg Signal

EMG signal-based pattern recognition (EMG-PR) techniques have gained lots of focus to develop myoelectric prosthesis. The performance of the prosthesis control-based applications mainly depends on extraction of eminent features with minimum neural information loss. The machine learning algorithms have a significant role to play for the development of Intelligent upper-limb prosthetic control (iULP) using EMG signal. This paper proposes a new technique of extracting the features known as advanced time derivative moments (ATDM) for effective pattern recognition of amputees. Four heterogeneous datasets have been used for testing and validation of the proposed technique. Out of the four datasets, three datasets have been taken from the standard NinaPro database and the fourth dataset comprises data collected from three amputees. The efficiency of ATDM features is examined with the help of Davies–Bouldin (DB) index for separability, classification accuracy and computational complexity. Further, it has been compared with similar work and the results reveal that ATDM features have excellent classification accuracy of 98.32% with relatively lower time complexity. The lower values of DB criteria prove the good separation of features belonging to various classes. The results are carried out on 2.6[Formula: see text]GHz Intel core i7 processor with MATLAB 2015a platform.

scholarly journals Optimizing strap designs of upper limb prostheses

2018 ◽  
Vol 10 (7) ◽  
pp. 168781401878926
Author(s):  
Ying Liu ◽  
Xiufeng Zhang ◽  
Ning Zhang ◽  
Jianguang Xu ◽  
Rong Yang
Keyword(s):  
Upper Limb ◽  
Original Problem ◽  
Mechanical Analysis ◽  
Equilibrium Equations ◽  
Prosthetic Socket ◽  
Upper Limb Prosthesis ◽  
Upper Limb Prostheses ◽  
Discipline Problem ◽  
The Individual ◽  
Limb Prosthesis

The precise positioning design of upper limb prostheses is important for patients with upper limb disability. In this study, we propose an upper limb prosthesis with a negative pressure design. Mechanical analysis is performed to obtain the force and moment equilibrium equations. Then, the individual discipline feasible method is performed to decouple the original problem into a three-sub-discipline problem. A minimum of three shoulder straps of tension is obtained during optimization using the Isight harness scheme. The prosthetic socket can be firmly attached to the human body. Further experiments verify that the proposed device meets the basic requirements of wearing.

Perceptions of cosmesis and function in adults with upper limb prostheses: a systematic literature review

2011 ◽  
Vol 35 (4) ◽  
pp. 332-341 ◽  
Author(s):  
Sophie Ritchie ◽  
Sally Wiggins ◽  
Alison Sanford
Keyword(s):  
Upper Limb ◽  
User Satisfaction ◽  
Future Design ◽  
Prosthetic Limb ◽  
Upper Limb Prosthesis ◽  
Upper Limb Prostheses ◽  
Technological Developments ◽  
User Groups ◽  
And Function ◽  
Satisfaction Rates

Background: Technological developments in prosthesis design of upper limb devices are improving rapidly, and understandings of user’s perceptions are important to reduce device abandonment and improve user satisfaction rates.Objectives: The purpose of this review was to establish what is known about adult user’s perceptions of upper limb prostheses in terms of both cosmesis and function.Study Design: Systematic review.Methods: A search of the literature between 1990 and 2010 identified over 600 possible citations; these were reduced to 15 citations based on selection criteria.Results: The main themes arising from the review were user satisfaction ratings with current prostheses, priorities for future design and the social implications of wearing a prosthetic limb. While users of cosmetic prostheses were mostly satisfied with their prostheses, satisfaction rates vary considerably across studies, due to variability in demographics of users and an ambiguity over the definitions of cosmesis and function. Design priorities also varied, though overall there is a slight trend toward prioritising function over cosmesis. The qualitative studies noted the importance users placed on presenting a ‘normal’ appearance and ‘not standing out’.Conclusions: The reviewed studies mostly examine functionality and cosmesis as separate constructs, and conclusions are limited due to the disparity of user groups studied. Recommendations are made for further work to explore understandings of these constructs in relation to upper limb prosthesis use.

scholarly journals An Electromyographically Controlled Elbow Locking Mechanism for an Upper Limb Prosthesis

1983 ◽  
Vol 10 (2-3) ◽  
pp. 87-93 ◽  
Author(s):  
H. T. Law ◽  
J. J. Hewson
Keyword(s):  
Upper Limb ◽  
Upper Arm ◽  
Hybrid Technology ◽  
Electronic Circuitry ◽  
Upper Limb Prosthesis ◽  
Locking Mechanism ◽  
Upper Limb Prostheses ◽  
Mechanical Devices ◽  
Limb Prosthesis

The purpose of the elbow lock incorporated in an artificial arm is discussed and the difficulties encountered in the operation of purely mechanical devices are outlined. An electrically driven locking mechanism has been built which is controlled by the electromyogram (e.m.g.) of the surviving muscles in the upper arm. Hybrid technology is ideally suited to the construction of the associated electronic circuitry and to many similar applications now being considered in attempts to improve the performance of upper-limb prostheses.

Self-Correcting Pattern Recognition System of Surface EMG Signals for Upper Limb Prosthesis Control

2014 ◽  
Vol 61 (4) ◽  
pp. 1167-1176 ◽  
Author(s):  
Sebastian Amsuss ◽  
Peter M. Goebel ◽  
Ning Jiang ◽  
Bernhard Graimann ◽  
Liliana Paredes ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Upper Limb ◽  
Recognition System ◽  
Surface Emg ◽  
Pattern Recognition System ◽  
Upper Limb Prosthesis ◽  
Prosthesis Control ◽  
Limb Prosthesis

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>

Sign in / Sign up

Export Citation Format

Share Document