Proposal of real-time Brillouin fiber sensing based on compressing sensing and pattern recognition algorithms

<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>

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

10.36227/techrxiv.12783101.v1 ◽

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>

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Real-Time and Simultaneous Control of Artificial Limbs Based on Pattern Recognition Algorithms

IEEE Transactions on Neural Systems and Rehabilitation Engineering ◽

10.1109/tnsre.2014.2305097 ◽

2014 ◽

Vol 22 (4) ◽

pp. 756-764 ◽

Cited By ~ 69

Author(s):

Max Ortiz-Catalan ◽

Bo Hkansson ◽

Rickard Brnemark

Keyword(s):

Pattern Recognition ◽

Real Time ◽

Artificial Limbs ◽

Recognition Algorithms ◽

Simultaneous Control

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A Content-Sensitive Approach to Search in Shared File Storages

Encyclopedia of Information Science and Technology, Second Edition ◽

10.4018/978-1-60566-026-4.ch123 ◽

2011 ◽

pp. 755-761

Author(s):

Gábor Richly ◽

Gábor Hosszú ◽

Ferenc Kovács

Keyword(s):

Pattern Recognition ◽

Real Time ◽

Measurement Data ◽

Stream Media ◽

Recognition Algorithms ◽

Novel Approach ◽

Experimental Media ◽

Shared File ◽

Audio Data ◽

Searching Method

The article presents a novel approach to search in shared audio file storages such as P2P-based systems. The proposed method enables the recognition of specific patterns in the audio contents, in such a way it extends the searching possibility from the description-based model to the content- based model. The targeted shared file storages seam to change contents rather unexpectedly. This volatile nature led our development to use real-time capable methods for the search process. The importance of the real-time pattern recognition algorithms that are used on audio data for content-sensitive searching in stream media has been growing over a decade (Liu, Wang, & Chen, 1998). The main problem of many algorithms is the optimal selection of the reference patterns (soundprints in our approach) used in the recognition procedure. This proposed method is based on distance maximization and is able to choose the pattern that later will be used as reference by the pattern recognition algorithms quickly (Richly, Kozma, Kovács & Hosszú, 2001). The presented method called EMESE (Experimental MEdia-Stream rEcognizer) is an important part of a lightweight content-searching method, which is suitable for the investigation of the network-wide shared file storages. This method was initially applied for real-time monitoring of the occurrence of known sound materials in broadcast audio. The experimental measurement data showed in the article demonstrate the efficiency of the procedure that was the reason for using it in shared audio database environment.

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