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.
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DOI：
10.3390/s16040592
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发表时间：
2016-04-23
期刊：
影响因子：
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通讯作者：
Olsson MC
中科院分区：
文献类型：
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作者：
Verikas A;Vaiciukynas E;Gelzinis A;Parker J;Olsson MC
这项研究分析了使用7号铁棒挥杆时，记录在8通道肌电图（EMG）信号流中的肌肉活动，并利用从EMG动态中提取的信息来预测最终击球的成功。15名高尔夫球手（每人击球5杆）考虑左右两侧腕屈肌、指共伸肌、菱形肌、斜方肌等手臂和肩部肌肉。提出了一种利用高斯滤波估计各通道肌电信号起始时间和分析激活序列的方法。每个球员的镜头都显示出肌肉持续活跃的模式。我们绘制了个人资料，并提供了关于玩家效率的见解。通过对每个通道的肌电动态检测发现，每个通道都有一对峰值作为高尔夫挥杆的标志，并介绍了一种用于自动提取挥杆段的峰值检测定制应用程序。构建了包含22个特征集的各种肌电特征。特征集可以单独使用，也可以在决策级融合中用于预测射击效果。在检测和回归任务中，采用随机森林作为学习器，研究了球头速度或球携带距离等目标属性的预测问题。检测是根据球员特定的平均水平来评估个人投篮的有效性，而回归是使用肌电图特征作为预测因子来估计目标属性的值。决策优化后的融合结果最好：速度和距离的检测错误率分别为24.3%和31.7%；回归的平均绝对百分比误差对速度为3.2%，对距离为6.4%。提出的肌电特征集被认为是有用的，特别是在组合使用时。特征集的排名显示了左右身体两侧肌肉活动的统计数据，基于相关性的肌电动力学分析和两个最高峰值的特性得出的特征，作为个人投篮效率的重要预测指标。激活序列图谱有助于分析高尔夫球击球过程中的肌肉协调，揭示特定的雪崩模式，但需要更多球员的数据才能得出更有力的结论。结果表明，从肌电信号流中产生的信息在预测高尔夫球击球成功方面是有用的，在杆头速度和球携带距离方面，具有可接受的精度。收集表面肌电数据的目的是自动评估高尔夫球手的表现，使可穿戴计算在环境智能领域成为可能，并有可能增强长距离驾驶的锻炼。
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.