A novel approach to surface electromyography: an exploratory study of electrode-pair selection based on signal characteristics

Kendell, Cynthia; Lemaire, Edward D.; Losier, Yves; Wilson, Adam; Chan, Adrian D. C.; Hudgins, B.

doi:10.1186/1743-0003-9-24

Cited by 59 publications

(48 citation statements)

References 18 publications

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“…Ties were given the same rank, with the next non-tied classifier being ranked by their position after accounting for the tied classifiers (e.g., a three-way tie at position three results in: 1, 2, 3, 3, 3, 6, 7, …) [48]. Rankings were summed across performance measures, with the lowest sum indicating the best classifier.…”

Section: Methodsmentioning

confidence: 99%

Faller Classification in Older Adults Using Wearable Sensors Based on Turn and Straight-Walking Accelerometer-Based Features

Drover

Howcroft

Kofman

et al. 2017

Sensors

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Faller classification in elderly populations can facilitate preventative care before a fall occurs. A novel wearable-sensor based faller classification method for the elderly was developed using accelerometer-based features from straight walking and turns. Seventy-six older individuals (74.15 ± 7.0 years), categorized as prospective fallers and non-fallers, completed a six-minute walk test with accelerometers attached to their lower legs and pelvis. After segmenting straight and turn sections, cross validation tests were conducted on straight and turn walking features to assess classification performance. The best “classifier model—feature selector” combination used turn data, random forest classifier, and select-5-best feature selector (73.4% accuracy, 60.5% sensitivity, 82.0% specificity, and 0.44 Matthew’s Correlation Coefficient (MCC)). Using only the most frequently occurring features, a feature subset (minimum of anterior-posterior ratio of even/odd harmonics for right shank, standard deviation (SD) of anterior left shank acceleration SD, SD of mean anterior left shank acceleration, maximum of medial-lateral first quartile of Fourier transform (FQFFT) for lower back, maximum of anterior-posterior FQFFT for lower back) achieved better classification results, with 77.3% accuracy, 66.1% sensitivity, 84.7% specificity, and 0.52 MCC score. All classification performance metrics improved when turn data was used for faller classification, compared to straight walking data. Combining turn and straight walking features decreased performance metrics compared to turn features for similar classifier model—feature selector combinations.

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Section: Methodsmentioning

confidence: 99%

Faller Classification in Older Adults Using Wearable Sensors Based on Turn and Straight-Walking Accelerometer-Based Features

Drover

Howcroft

Kofman

et al. 2017

Sensors

View full text Add to dashboard Cite

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“…A ranking method similar to the approach used in Kendell et al, 2012 [21] was employed to determine the best models. Each model evaluation parameter was ranked from best (1) to worst ( n ), and ranks for all model evaluation parameters were summed to identify the overall best model (lowest summed rank) (Fig.…”

Section: Methodsmentioning

confidence: 99%

Feature selection for elderly faller classification based on wearable sensors

Howcroft

Kofman

Lemaire

2017

J NeuroEngineering Rehabil

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BackgroundWearable sensors can be used to derive numerous gait pattern features for elderly fall risk and faller classification; however, an appropriate feature set is required to avoid high computational costs and the inclusion of irrelevant features. The objectives of this study were to identify and evaluate smaller feature sets for faller classification from large feature sets derived from wearable accelerometer and pressure-sensing insole gait data.MethodsA convenience sample of 100 older adults (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, left and right shanks. Feature selection was performed using correlation-based feature selection (CFS), fast correlation based filter (FCBF), and Relief-F algorithms. Faller classification was performed using multi-layer perceptron neural network, naïve Bayesian, and support vector machine classifiers, with 75:25 single stratified holdout and repeated random sampling.ResultsThe best performing model was a support vector machine with 78% accuracy, 26% sensitivity, 95% specificity, 0.36 F1 score, and 0.31 MCC and one posterior pelvis accelerometer input feature (left acceleration standard deviation). The second best model achieved better sensitivity (44%) and used a support vector machine with 74% accuracy, 83% specificity, 0.44 F1 score, and 0.29 MCC. This model had ten input features: maximum, mean and standard deviation posterior acceleration; maximum, mean and standard deviation anterior acceleration; mean superior acceleration; and three impulse features. The best multi-sensor model sensitivity (56%) was achieved using posterior pelvis and both shank accelerometers and a naïve Bayesian classifier. The best single-sensor model sensitivity (41%) was achieved using the posterior pelvis accelerometer and a naïve Bayesian classifier.ConclusionsFeature selection provided models with smaller feature sets and improved faller classification compared to faller classification without feature selection. CFS and FCBF provided the best feature subset (one posterior pelvis accelerometer feature) for faller classification. However, better sensitivity was achieved by the second best model based on a Relief-F feature subset with three pressure-sensing insole features and seven head accelerometer features. Feature selection should be considered as an important step in faller classification using wearable sensors.

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“…Similarly, RMS appears to be the best parameter compared to MAV, MAX, SSC, ZC and WL as it provides a quantitative measure for electrode selection [41], thus delivering the best performance for facial gestures of EMG signals [49]. On the other hand, Integrated EMG (IEMG) features used to determine an increase in signal period, power and amplitude reflects a higher muscle fiber recruitment for a fixed external force [19].…”

Section: Automated Emg Analysismentioning

confidence: 99%

A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions

Nazmi

Rahman

Yamamoto

et al. 2016

Sensors

303

177

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In recent years, there has been major interest in the exposure to physical therapy during rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and human machine interface (HMI) applications. An automated system will guide the user to perform the training during rehabilitation independently. Advances in engineering have extended electromyography (EMG) beyond the traditional diagnostic applications to also include applications in diverse areas such as movement analysis. This paper gives an overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who would like to select the most appropriate methodology in classifying motion patterns, especially during different types of contractions. For feature extraction, the probability density function (PDF) of EMG signals will be the main interest of this study. Following that, a brief explanation of the different methods for pre-processing, feature extraction and classifying EMG signals will be compared in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

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A novel approach to surface electromyography: an exploratory study of electrode-pair selection based on signal characteristics

Cited by 59 publications

References 18 publications

Faller Classification in Older Adults Using Wearable Sensors Based on Turn and Straight-Walking Accelerometer-Based Features

Faller Classification in Older Adults Using Wearable Sensors Based on Turn and Straight-Walking Accelerometer-Based Features

Feature selection for elderly faller classification based on wearable sensors

A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions

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