This study investigated the application of Symbolic Aggregate approXimation (SAX) to modelling dynamic body motion using a single inertial measurement unit (IMU). In addition this study demonstrates how IMUs located at different positions around the body produce comparable results. This study investigates the output of multiple IMU sensors, employed to monitor movement. Next a comparison of the sternum, pelvis, head and lower back sensor locations is conducted by analysing the measured rotation and position IMU data. Additionally, the classifier has been improved by increasing the information in the training data to avoid incorrect classification of similar activities. The results obtained in this study also prove that the sternum and head sensors provided comparable data to the pelvis sensor when using TSBs for classification, especially when used to classify dynamic activities. To pre-process the data, sub-dimensional motif discovery was employed to find features within the data from multiple IMUs. This improves on previous studies which illustrated difficulty classifying fast movements using the sternum IMU. This data was also approximated using SAX and classified by comparing Time Series Bitmaps (TSB's) to find the least Euclidean distance between the reference TSB's and the sliding window TSB's.