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Poor medication adherence threatens an individual's health and is responsible for substantial medical costs in the United States annually. In order to improve medication adherence rates and provide timely reminders, we developed a smartwatch application that collects data from embedded inertial sensors, which include an accelerometer and gyroscope, to monitor a series of actions happening during an individual's medication intake. After the collected data was delivered to a server, Apache Spark was used to distribute the data and apply machine learning algorithms in order to predict several discrete actions including medication intake. By utilizing these tools, we were able to preprocess high frequency sensor data and apply a random forest algorithm, yielding high frequency and recall of the aforementioned actions.