A Windowed Eigenspectrum Method for Multivariate sEMG Classification During Reaching Movements

Chiang, Joyce; Wang, Z.J.; McKeown, Martin J.

doi:10.1109/lsp.2008.917801

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Prostheses control by mapping EMG signals into several classes of forearm motions [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ], evaluation of local muscle fatigue in static [ 20 , 21 , 22 , 23 , 24 ], as well as dynamic exercises [ 25 , 26 , 27 , 28 , 29 ], screening for neuromuscular disorders [ 30 , 31 , 32 , 33 ] and analysis of the gait of arthritic patients [ 34 , 35 ] are probably the main application fields. Prediction of externally-applied forces to human hands by relating the EMG signals and experimentally known forces [ 36 ], prediction of forces on the lumbar spine [ 37 ], estimation of the iso-kinetic knee torque [ 38 ], recognition of simple and complex finger flexions [ 39 ], anticipation of head motion [ 40 ] and finger joint angle [ 41 ] for virtual-environment applications, distinguishing between normal subjects and subjects that experienced stroke of several degrees of severity (severe, moderate and mild) [ 42 ], measuring age-related reduction in number of motor units [ 43 ], a computer interface for text typing based on EMG signals induced by wrist movements [ 44 ], hand gesture sensing [ 45 ], assessing the subject’s vigilance level [ 46 ], stride detection and stride length estimation during walking [ 42 ] and classification of different tremor types [ 47 ] are other recent examples of using EMG data-ba...…”

Section: Introductionmentioning

confidence: 99%

Electromyographic Patterns during Golf Swing: Activation Sequence Profiling and Prediction of Shot Effectiveness

Verikas

Vaičiukynas

Gelžinis

et al. 2016

Sensors

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This study analyzes muscle activity, recorded in an eight-channel electromyographic (EMG) signal stream, during the golf swing using a 7-iron club and exploits information extracted from EMG dynamics to predict the success of the resulting shot. Muscles of the arm and shoulder on both the left and right sides, namely flexor carpi radialis, extensor digitorum communis, rhomboideus and trapezius, are considered for 15 golf players (∼5 shots each). The method using Gaussian filtering is outlined for EMG onset time estimation in each channel and activation sequence profiling. Shots of each player revealed a persistent pattern of muscle activation. Profiles were plotted and insights with respect to player effectiveness were provided. Inspection of EMG dynamics revealed a pair of highest peaks in each channel as the hallmark of golf swing, and a custom application of peak detection for automatic extraction of swing segment was introduced. Various EMG features, encompassing 22 feature sets, were constructed. Feature sets were used individually and also in decision-level fusion for the prediction of shot effectiveness. The prediction of the target attribute, such as club head speed or ball carry distance, was investigated using random forest as the learner in detection and regression tasks. Detection evaluates the personal effectiveness of a shot with respect to the player-specific average, whereas regression estimates the value of target attribute, using EMG features as predictors. Fusion after decision optimization provided the best results: the equal error rate in detection was 24.3% for the speed and 31.7% for the distance; the mean absolute percentage error in regression was 3.2% for the speed and 6.4% for the distance. Proposed EMG feature sets were found to be useful, especially when used in combination. Rankings of feature sets indicated statistics for muscle activity in both the left and right body sides, correlation-based analysis of EMG dynamics and features derived from the properties of two highest peaks as important predictors of personal shot effectiveness. Activation sequence profiles helped in analyzing muscle orchestration during golf shot, exposing a specific avalanche pattern, but data from more players are needed for stronger conclusions. Results demonstrate that information arising from an EMG signal stream is useful for predicting golf shot success, in terms of club head speed and ball carry distance, with acceptable accuracy. Surface EMG data, collected with a goal to automatically evaluate golf player’s performance, enables wearable computing in the field of ambient intelligence and has potential to enhance exercising of a long carry distance drive.

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Section: Introductionmentioning

confidence: 99%

Electromyographic Patterns during Golf Swing: Activation Sequence Profiling and Prediction of Shot Effectiveness

Verikas

Vaičiukynas

Gelžinis

et al. 2016

Sensors

View full text Add to dashboard Cite

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Towards a Versatile Surface Electromyography Classification System

Barmpakos

Στριμπάκος

Karkanis

et al. 2016

XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016

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A novel fuzzy approach for automatic Brunnstrom stage classification using surface electromyography

Liparulo

Zhang

Panella

et al. 2016

Med Biol Eng Comput

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Clinical assessment plays a major role in post-stroke rehabilitation programs for evaluating impairment level and tracking recovery progress. Conventionally, this process is manually performed by clinicians using chart-based ordinal scales which can be both subjective and inefficient. In this paper, a novel approach based on fuzzy logic is proposed which automatically evaluates stroke patients' impairment level using single-channel surface electromyography (sEMG) signals and generates objective classification results based on the widely used Brunnstrom stages of recovery. The correlation between stroke-induced motor impairment and sEMG features on both time and frequency domain is investigated, and a specifically designed fuzzy kernel classifier based on geometrically unconstrained membership function is introduced in the study to tackle the challenges in discriminating data classes with complex separating surfaces. Experiments using sEMG data collected from stroke patients have been carried out to examine the validity and feasibility of the proposed method. In order to ensure the generalization capability of the classifier, a cross-validation test has been performed. The results, verified using the evaluation decisions provided by an expert panel, have reached a rate of success of the 92.47%. The proposed fuzzy classifier is also compared with other pattern recognition techniques to demonstrate its superior performance in this application.

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A Windowed Eigenspectrum Method for Multivariate sEMG Classification During Reaching Movements

Cited by 3 publications

References 12 publications

Electromyographic Patterns during Golf Swing: Activation Sequence Profiling and Prediction of Shot Effectiveness

Electromyographic Patterns during Golf Swing: Activation Sequence Profiling and Prediction of Shot Effectiveness

Towards a Versatile Surface Electromyography Classification System

A novel fuzzy approach for automatic Brunnstrom stage classification using surface electromyography

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