Classification complexity in myoelectric pattern recognition

Nilsson, Niclas; Hȧkansson, Bo; Ortiz-Catalán, Max

doi:10.1186/s12984-017-0283-5

Cited by 24 publications

(25 citation statements)

References 27 publications

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“… Data analysis tool using CCEAs [26] . Section 1 illustrates the distance of each class and its closest neighbors in feature space (2D).…”

Section: Resultsmentioning

confidence: 99%

“…It is known that features with high class separability improve recognition accuracy. Therefore, the data analysis tools including CCEAs [26] and PCA were further used to evaluate class separabilities of different combinations of features.…”

Section: Gesture Recognition and Controlmentioning

confidence: 99%

“…For instance, although the relevance and redundancy of features were investigated in previous studies [23] , [24] , specific conflicting classes still cannot be informed. Thus, classification complexity estimating algorithms (CCEAs) [26] and principal components analysis (PCA) were applied to estimate the class separability of different feature sets in this work.…”

Section: Introductionmentioning

confidence: 99%

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An IoT-Enabled Stroke Rehabilitation System Based on Smart Wearable Armband and Machine Learning

Yang

Deng

Pang

et al. 2018

IEEE J. Transl. Eng. Health Med.

136

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Surface electromyography signal plays an important role in hand function recovery training. In this paper, an IoT-enabled stroke rehabilitation system was introduced which was based on a smart wearable armband (SWA), machine learning (ML) algorithms, and a 3-D printed dexterous robot hand. User comfort is one of the key issues which should be addressed for wearable devices. The SWA was developed by integrating a low-power and tiny-sized IoT sensing device with textile electrodes, which can measure, pre-process, and wirelessly transmit bio-potential signals. By evenly distributing surface electrodes over user’s forearm, drawbacks of classification accuracy poor performance can be mitigated. A new method was put forward to find the optimal feature set. ML algorithms were leveraged to analyze and discriminate features of different hand movements, and their performances were appraised by classification complexity estimating algorithms and principal components analysis. According to the verification results, all nine gestures can be successfully identified with an average accuracy up to 96.20%. In addition, a 3-D printed five-finger robot hand was implemented for hand rehabilitation training purpose. Correspondingly, user’s hand movement intentions were extracted and converted into a series of commands which were used to drive motors assembled inside the dexterous robot hand. As a result, the dexterous robot hand can mimic the user’s gesture in a real-time manner, which shows the proposed system can be used as a training tool to facilitate rehabilitation process for the patients after stroke.

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“… Data analysis tool using CCEAs [26] . Section 1 illustrates the distance of each class and its closest neighbors in feature space (2D).…”

Section: Resultsmentioning

confidence: 99%

Section: Gesture Recognition and Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An IoT-Enabled Stroke Rehabilitation System Based on Smart Wearable Armband and Machine Learning

Yang

Deng

Pang

et al. 2018

IEEE J. Transl. Eng. Health Med.

136

View full text Add to dashboard Cite

show abstract

“…This new extensive fault-tolerant system could use any other algorithm. The SVM is widely used in sEMG signals [ 46 , 68 , 69 , 70 , 71 , 72 ] and was selected to compare and evaluate the effectiveness of this new system.…”

Section: Resultsmentioning

confidence: 99%

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

Moura

Balbinot

2018

Sensors

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A few prosthetic control systems in the scientific literature obtain pattern recognition algorithms adapted to changes that occur in the myoelectric signal over time and, frequently, such systems are not natural and intuitive. These are some of the several challenges for myoelectric prostheses for everyday use. The concept of the virtual sensor, which has as its fundamental objective to estimate unavailable measures based on other available measures, is being used in other fields of research. The virtual sensor technique applied to surface electromyography can help to minimize these problems, typically related to the degradation of the myoelectric signal that usually leads to a decrease in the classification accuracy of the movements characterized by computational intelligent systems. This paper presents a virtual sensor in a new extensive fault-tolerant classification system to maintain the classification accuracy after the occurrence of the following contaminants: ECG interference, electrode displacement, movement artifacts, power line interference, and saturation. The Time-Varying Autoregressive Moving Average (TVARMA) and Time-Varying Kalman filter (TVK) models are compared to define the most robust model for the virtual sensor. Results of movement classification were presented comparing the usual classification techniques with the method of the degraded signal replacement and classifier retraining. The experimental results were evaluated for these five noise types in 16 surface electromyography (sEMG) channel degradation case studies. The proposed system without using classifier retraining techniques recovered of mean classification accuracy was of 4% to 38% for electrode displacement, movement artifacts, and saturation noise. The best mean classification considering all signal contaminants and channel combinations evaluated was the classification using the retraining method, replacing the degraded channel by the virtual sensor TVARMA model. This method recovered the classification accuracy after the degradations, reaching an average of 5.7% below the classification of the clean signal, that is the signal without the contaminants or the original signal. Moreover, the proposed intelligent technique minimizes the impact of the motion classification caused by signal contamination related to degrading events over time. There are improvements in the virtual sensor model and in the algorithm optimization that need further development to provide an increase the clinical application of myoelectric prostheses but already presents robust results to enable research with virtual sensors on biological signs with stochastic behavior.

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“…A larger statistical distance implies greater separability between classes. We designated DB≥0.9 as the threshold value for significant separability between phasor clusters, as this value has been used to develop classification models that perform with high accuracy (∼90%) 36 …”

Section: Methodsmentioning

confidence: 99%

Cerebral metabolism in a mouse model of Alzheimer’s disease characterized by two-photon fluorescence lifetime microscopy of intrinsic NADH

Gómez

Sakadžić

et al. 2018

Neurophoton.

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Abstract.Disruptions and alterations to cerebral energy metabolism play a vital role in the onset and progression of many neurodegenerative disorders and cerebral pathologies. In order to precisely understand the complex alterations underlying Alzheimer’s disease (AD) progression, in vivo imaging at the microscopic level is required in preclinical animal models. Utilizing two-photon fluorescence lifetime imaging microscopy and the phasor analysis method, we have observed AD-related variations of endogenous fluorescence of reduced nicotinamide adenine dinucleotide (NADH) in vivo. We collected NADH FLIM images from the cerebral cortices of both APPswe:PS1dE9 mice to model amyloid β plaque accumulation and corresponding age-matched wildtype controls. Distinct variations in NADH fluorescence lifetime between wildtype and AD mice, as well as variations related to proximity from amyloid plaques, are obvervable via the phasor method. The combination of NADH FLIM and phasor analysis allows for a minimally invasive, high-resolution technique to characterize the adverse effects of amyloid β accumulation on mitochondrial energy metabolism and could guide our understanding of preclinical AD pathology.

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Classification complexity in myoelectric pattern recognition

Cited by 24 publications

References 27 publications

An IoT-Enabled Stroke Rehabilitation System Based on Smart Wearable Armband and Machine Learning

An IoT-Enabled Stroke Rehabilitation System Based on Smart Wearable Armband and Machine Learning

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

Cerebral metabolism in a mouse model of Alzheimer’s disease characterized by two-photon fluorescence lifetime microscopy of intrinsic NADH

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