  Poor medication adherence presents serious economic and health problems
including compromised treatment effectiveness, medical complications, and loss
of billions of dollars in wasted medicine or procedures. Though various
interventions have been proposed to address this problem, there is an urgent
need to leverage light, smart, and minimally obtrusive technology such as
smartwatches to develop user tools to improve medication use and adherence. In
this study, we conducted several experiments on medication-taking activities,
developed a smartwatch android application to collect the accelerometer hand
gesture data from the smartwatch, and conveyed the data collected to a central
cloud database. We developed neural networks, then trained the networks on the
sensor data to recognize medication and non-medication gestures. With the
proposed machine learning algorithm approach, this study was able to achieve
average accuracy scores of 97% on the protocol-guided gesture data, and 95% on
natural gesture data.
