| 1. |
- Ražanskas, Petras, et al.
(författare)
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Predicting physiological parameters in fatiguing bicycling exercises using muscle activation timing
- 2017
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Ingår i: Biomedical Signal Processing and Control. - Amsterdam : Elsevier. - 1746-8094 .- 1746-8108. ; 35, s. 19-29
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Tidskriftsartikel (refereegranskat)abstract
- This article is concerned with a novel technique for prediction of blood lactate concentration level and oxygen uptake rate from multi-channel surface electromyography (sEMG) signals. The approach is built on predictive models exploiting a set of novel time-domain variables computed from sEMG signals. Signals from three muscles of each leg, namely, vastus lateralis, rectus femoris, and semitendinosus were used in this study. The feature set includes parameters reflecting asymmetry between legs, phase shifts between activation of different muscles, active time percentages, and sEMG amplitude. Prediction ability of both linear and non-linear (random forests-based) models was explored. The random forests models showed very good prediction accuracy and attained the coefficient of determination R2 = 0.962 for lactate concentration level and R2 = 0.980 for oxygen uptake rate. The linear models showed lower prediction accuracy. Comparable results were obtained also when sEMG amplitude data were removed from the training sets. A feature elimination algorithm allowed to build accurate random forests (R2 > 0.9) using just six (lactate concentration level) or four (oxygen uptake rate) time-domain variables. Models created to predict blood lactate concentration rate relied on variables reflecting interaction between front and back leg muscles, while parameters computed from front muscles and interactions between two legs were the most important variables for models created to predict oxygen uptake rate.© 2017 Elsevier Ltd.
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| 2. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Electromyographic Patterns during Golf Swing : Activation Sequence Profiling and Prediction of Shot Effectiveness
- 2016
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Ingår i: Sensors. - Basel : MDPI AG. - 1424-8220. ; 16:4
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 3. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Exploring relations between EMG and biomechanical data recorded during a golf swing
- 2017
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Ingår i: Expert systems with applications. - Kidlington, Oxford : Pergamon Press. - 0957-4174 .- 1873-6793. ; 88, s. 109-117
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Tidskriftsartikel (refereegranskat)abstract
- Exploring relations between patterns of peak rotational speed of thorax, pelvis and arm, and patterns of EMG signals recorded from eight muscle regions of forearms and shoulders during the golf swing is the main objective of this article. The linear canonical correlation analysis, allowing studying relations between sets of variables, was the main technique applied. To get deeper insights, linear and nonlinear random forests-based prediction models relating a single output variable, e.g. a thorax peak rotational speed, with a set of input variables, e.g. an average intensity of EMG signals were used. The experimental investigations using data from 16 golfers revealed statistically significant relations between sets of input and output variables. A strong direct linear relation was observed between lin- ear combinations of EMG averages and peak rotational speeds. The coefficient of determination values R2 = 0 . 958 and R2 = 0 . 943 obtained on unseen data by the random forest models designed to predict peak rotational speed of thorax and pelvis , indicate high modelling accuracy. However, predictions of peak rotational speed of arm were less accurate. This was expected, since peak rotational speed of arm played a minor role in the linear combination of peak speeds. The most important muscles to predict peak rotational speed of the body parts were identified. The investigations have shown that the canon- ical correlation analysis is a promising tool for studying relations between sets of biomechanical and EMG data. Better understanding of these relations will lead to guidelines concerning muscle engagement and coordination of thorax, pelvis and arms during a golf swing and will help golf coaches in providing substantiated advices. ©2017 Elsevier Ltd. All rights reserved.
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