Aggressive motion can occur in clinical and elderly care settings with people suffering from dementia, mental disorders, or other conditions that affect memory. Since identifying the nature of the event can be difficult with people who have memory and communication issues, other methods to identify and record aggressive motion would be useful for care providers to reduce re-occurrences of this activity. A wearable technology approach for human activity recognition was explored in this thesis to detect aggressive movements. This approach aims to provide a means to identify the person that initiated aggressive motion and to categorize the aggressive action. The main objective of this thesis was to determine the effectiveness of smartwatch accelerometer and gyroscope sensor data for classifying aggressive and non-aggressive activities. 30 able-bodied participants donned two Microsoft Bands 2 smartwatches and performed an activity circuit of similar aggressive and non-aggressive movements. Statistical and physical features were extracted from the smartwatch sensors signals, and subsequently used by multiple classifiers to determine on a machine learning platform six performance metrics (accuracy, sensitivity, specificity, precision, F-score, Matthews correlation coefficient). This thesis demonstrated: 1) the best features for a binary classification; 2) the best and most practical machine learning classifier and feature selector model; 3) the evaluation metrics differences between unilateral smartwatch and bilateral smartwatches; 4) the most suitable machine learning algorithm for a multinomial classification.