Methodology and Results

In order to develop an approach to activity classification the exercise routine performed by each athlete was segmented and annotated for all activities and used to create a training set. The acceleration data from the two WIMUs

Activity a b c d e f a = Agility Cut 166 0 6 4 3 1 b = Walking 0 399 0 0 0 0 c = Jumping on box 4 2 17 3 1 2 d = Jogging 0 0 0 205 0 0 e = Sprinting 1 0 0 0 27 0 f = Ball Kicking 2 4 5 5 3 68

Table 15: Confusion Matrix for the classifier using a single sensor on the shank

was isolated and features extracted for classification purposes. An early fusion approach was adopted to fuse the two feature vectors from each accelerometer. More detail about different fusion schemes is described in the next section. A window length of three seconds was chosen as this was sufficient time for each of the selected training activities to be completed. The DWT was used with much success in extracting discriminative features from accelerometer data in section 3.3.3 and thus was used to extract features for classification. The Daubechies 4 wavelet is a popular mother wavelet choice in signal analysis problems due to its regularity and fast computational time, and was chosen in this work.

The F-measure scores when using data solely from the leg shank sensor is presented in Table 18 and the confusion matrix associated is presented in Table 15. Similarly the scores from the thigh sensor is presented in Table 16 and its confusion matrix is in Table 19. Table 20 shows the F-measure scores when data from both the leg shank sensor and leg thigh sensor are both used to classify the activity being performed. All values in this experiment were computed using a ten-fold cross validation. Since the classifier was trained with classes which had different instance populations the F-measure scores are shown. The F-measure score gives a better indication of a model’s ability to correctly identify an activity than standard classification accuracy alone.

Comparing Table 15, Table 16 and Table 17, it is possible to see the ac- tivities that require two sensors for accurate classification. Agility cut and

Activity a b c d e f a = Agility Cut 176 0 2 0 0 2 b = Walking 0 399 0 0 0 0 c = Jumping on box 3 2 21 0 0 3 d = Jogging 0 0 0 205 0 0 e = Sprinting 0 0 0 0 28 0 f = Ball Kicking 4 3 5 4 1 70

Table 16: Confusion Matrix for the classifier using a single sensor on the thigh

Activity a b c d e f a = Agility Cut 180 0 0 0 0 0 b = Walking 0 399 0 0 0 0 c = Jumping on box 0 0 27 2 0 0 d = Jogging 0 0 0 205 0 0 e = Sprinting 0 0 0 0 28 0 f = Ball Kicking 3 5 2 3 1 73 Table 17: Confusion Matrix for the classifier using two sensors jumping on a box are much more complex activities than walking or jogging and more information is required to distinguish those activities from the rest. The sensor on the thigh would capture the more pronounced movement re- quired before a jump is undertaken. The thigh sensor also aids in classifying agility cuts as the single sensor approach confuses this activity with walking, jumping on a box, jogging and sprinting whereas the two sensor approach has a 100% success rate recognising this activity. Both approaches perform similarly when attempting to recognise a subject kicking a ball. One reason why this confusion could occur is the varied kicking style between subjects. No specification was made hence right foot, left foot, inside of the foot strike, laces strike, passing, shot, cross, chip etc. are all viable methods that lie in the “ball kicking” label. A larger number of subjects in the dataset and a more specified activity would help account for the variation in kicking styles. Tables 18 and Table 20 show the precision, recall and F-measure scores for both approaches. The two sensor approach has a consistent classification accuracy rate across all activities unlike the single sensor approaches. Figure

Activity Precision Recall F-Measure Agility Cut 0.96 0.922 0.941 Walking 0.985 1 0.993 Jumping on box 0.607 0.586 0.596 Jogging 0.945 1 0.972 Sprinting 0.794 0.964 0.871 Ball Kicking 0.958 0.782 0.861

Table 18: Precision, Recall and F1 score obtained post classification using a

single sensor on the shank.

Activity Precision Recall F-Measure Agility Cut 0.962 0.978 0.97 Walking 0.988 1 0.994 Jumping on box 0.75 0.724 0.737 Jogging 0.981 1 0.99 Sprinting 0.966 1 0.982 Ball Kicking 0.933 0.805 0.864

Table 19: Precision, Recall and F1 score obtained post classification using a

single sensor on the thigh.

25 illustrates the difference in accuracy between using a single sensor and using two strategically placed sensors.

4.3.4 Conclusion

In this section a novel body worn inertial sensor framework capable of auto- matically segmenting and classifying various actions in outdoor unconstrained environments is described. Sensors have been used extensively in body moni- toring applications. With sensors becoming cheaper and more available, there

Activity Precision Recall F-Measure Agility Cut 0.984 1 0.992 Walking 0.988 1 0.994 Jumping on box 0.931 0.931 0.931 Jogging 0.976 1 0.988 Sprinting 0.966 1 0.982 Ball Kicking 1 0.839 0.913

Table 20: Precision, Recall and F1 score obtained post classification using

Figure 25: F1 score comparison between one sensor and two sensors

are a variety of applications that can benefit from using two or more sensors that can capture physiological information from strategic locations. In this experiment the use of one inertial sensor versus two strategically placed in- ertial sensors is evaluated. The use of a second sensor has allowed in this experiment for complex activities to be recognised.