A Quantitative Model To Evaluate Wrist-Rotation In Golf (P6)

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Experimental Verification

In this section, we describe our methods of data collection, model generation and validation to provide feedback on the quality of movements with respect to wrist rotation.

Experimental Procedure

We conducted our experiments to express the quality of the golf swing with respect to the wrist rotation. The experiments were conducted on three male subjects and one female subject all aged between 20 and 35. Each of the subjects wore three on-body sensors. In addition, two sensor nodes were placed on the golf club: one on the club head and one on the grip as shown in Fig. 4. The subjects were asked to perform the golf swing ten times for each of the variations listed in Table 1.

Experimental movements
Table 1 Experimental movements

Our subjects performed swings after first addressing the ball with 20◦, 40◦, 60◦ and 80◦ clockwise and counter-clockwise rotation of the wrists. Each subject also performed a perfect golf swing that has no wrist rotation or out-of-plane movements. For each movement, the amount of wrist rotation was controlled by fixing the location of the nodes placed on the golf club. The subjects must grip the club aligned with the nodes on the club. They were asked to keep their wrist fixed throughout the movements. This allows the system to control the swing plane while achieving consistent angles in different segments of the swing. All of the swings were performed in the absence of a golf ball. The subjects were also asked to perform the swings at a specified speed for experimental consistency.

Statistical and morphological features
Table 2 Statistical and morphological features

An extra mote was connected to a laptop via USB port to collect data from all sensor nodes.The data was collected using our tool developed in MATLAB. We followed the procedure for data collection, preprocessing, feature extraction, model generation, and validation as described previously. We processed collected data offline using our tools developed in MATLAB.

Quantification Results

For each trial, the data collected from four subjects was first preprocessed using a five-point moving average filter to remove the effect of noise. Each trial was divided into four major segments consisting of takeaway, backswing, downswing and follow-through. The manual segmentation was performed with the help of the video recorded during data collection. An exhaustive set of features was extracted from each segment. The features include statistical and morphological features as shown in Table 2 in which the first eleven features represent statistical features obtained from each signal segment, and the next four features are morphological features extracted from ten evenly distributed samples over each segment. We used 50% of the trials for the training to build our quantitative model,andthe rest to evaluate performance of the model.

For each of the major segments, a separate quantitative model was built. The features extracted from five sensors (x,y,z accelerometer, and x,y gyroscope) formed a 215-dimensional feature space for each sensor node. Data fusion was used to combine features from all sensor nodes to form a 1075 dimensional feature space which was used for subsequent processing. The features were fed to the PCA block for dimension reduction. Only a small number of principal components obtained from PCA were used to find LDA projections. The number of principal components was set to the rank of the within-class scatter matrix.

Given nine different groups of wrist rotation, LDA creates eight discriminant functions in the form of linear combinations of the input. In Fig. 8 we illustrate projections of the training trials using the first two dimensions for takeaway, backswing, downswing and follow-through. The group 1 indicated by green color corresponds to perfect swings while red represented by groups 2, 3 …5 and magenta colors annotated by 6, 7 …9 show clockwise and counter clockwise rotations respectively. These figures demonstrate the effectiveness of our technique in distinguishing different variations of the wrist rotation. Furthermore, the graphs would clearly describe the angular rotation.

The projections obtained by applying LDA were used to build a linear regression as described previously. We used the validation set to measure the degree of wrist rotation based on the model acquired. The values of error in terms of RMSE and MAE are shown in Table 3 and Table 4 respectively. In overall, the amount of root mean squared error was 15.5, 10.7, 8.9 and 9.1 for takeaway (TA), backswing (BS), downswing (DS) and follow-through (FT) respectively. The overall value of absolute mean error was reported as 9.2, 7.7, 6.6 and 6.5 degrees for TA, BS, DS and FT respectively which introduces an average error of less than 10 degrees for all segments.

Frequency Adjustment

Throughout our experiments, we used a sampling frequency of 50Hz which provides good resolution in capturing motions of golf swing. Reducing the sampling frequency can potentially reduce the complexity of processing. However, over-reduction may eliminate important details of the signal. In an effort to address this issue, we further adjusted our sampling rate with respect to the performance of our model. Recall the performance of our model expressed in terms of RMSE and MAE, our adjustment processtends to find a minimum sampling frequency that maintains approximately similar performance to 50Hz.


Projection of training trials for different swing segments using LDA
Fig. 8. Projection of training trials for different swing segments using LDA
RMSE values for different swing segments
Table 3 RMSE values for different swing segments
values for different swing segments
Table 4 MAE values for different swing segments

For the purpose of frequency adjustment, we measured RMSE and MAE errors for different sampling frequencies between 5Hz and 50Hz. The results are illustrated in Fig. 9 and Fig. 10. For each segment, the error remained almost constant beyond certain frequency. This threshold varied from one segment to another.The lowest threshold was obtainedfor takeaway (10Hz) and the highest frequency belonged to downswing and follow-through (30Hz).

Root mean squared error vs. sampling frequency for different segments
Fig.9.Root mean squared error vs. sampling frequency for different segments
Mean absolute error vs. sampling frequency for different swing segments
Fig. 10. Mean absolute error vs. sampling frequency for different swing segments

The difference between minimum sampling frequencies is mainly a factor of changes in speed of swing motions from one segment to another. According to the analysis performed using highs peedcine-films of tournament professionals, the golf club can move four times faster during downswing than it usually does during takeaway and backswing. As a result, faster motions require higher sampling frequencies to ensure the collected data has acceptable resolution. Considering the worst case (i.e. frequency required for downswing and follow-through), our system allows a frequency of 30Hz while maintaining the same amount of error as reported at 50Hz.


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