9-axis Magnetic, Angular Rate and Gravity (MARG) sensors are an ideal choice for movement tracking. Inexpensive while allowing for a low profile and ergonomic form factor, they have been integrated into devices across various applications and domains including wrist-worn smart watches, fitness trackers, smart phones, ariel drones, and robotics. This report investigates the implementation of a system deploying wireless bodily mounted MARGs for the purpose of classifying human movements, in this case movements typical of an intensive weightlifting regiment. Using off the shelf components, four 3D printed housings containing printed circuit boards (PCBs) are mounted on both wrists and ankles. Their data is transmitted wirelessly over Bluetooth Low Energy (BLE) protocol to be received by a BLE supporting central device. Data is stored in a local MongoDB database on a windows laptop, then the dataset is exported to .csv format for cleaning and classification using Pandas, Numpy, and high level PyTorch libraries. Implementations of several proven timeseries classification algorithms are tested on the collected dataset to evaluate its robustness. The results of this testing show that the dataset comprising 166 samples over 28 classes is at its early stages, with classification testing achieving 39% accuracy over training and validation data. Remapping the labels onto 2 classes results in 79% accuracy. Further work should include additional data collection, data cleaning, hierarchical labelling, and feature engineering.