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Electromyographic Patterns during Golf Swing: Activation Sequence Profiling and Prediction of Shot Effectiveness
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Kaunas University of Technology, Kaunas, Lithuania.ORCID-id: 0000-0003-2185-8973
Kaunas University of Technology, Kaunas, Lithuania.
Kaunas University of Technology, Kaunas, Lithuania.
Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Bio- och miljösystemforskning (BLESS).ORCID-id: 0000-0003-1184-5036
Vise andre og tillknytning
2016 (engelsk)Inngår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 16, nr 4, artikkel-id 592Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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.

sted, utgiver, år, opplag, sider
Basel: MDPI AG , 2016. Vol. 16, nr 4, artikkel-id 592
Emneord [en]
EMG, muscle activity onset, peak detection, random forest, decision fusion
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-31870DOI: 10.3390/s16040592ISI: 000375153700171PubMedID: 27120604Scopus ID: 2-s2.0-84964308572OAI: oai:DiVA.org:hh-31870DiVA, id: diva2:955929
Forskningsfinansiär
Knowledge Foundation, 2012/0319Tilgjengelig fra: 2016-08-27 Laget: 2016-08-27 Sist oppdatert: 2018-10-22bibliografisk kontrollert

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