Recognizing Soldier Activities in the Field

We describe the activity recognition component of the Soldier Assist System (SAS), which was built to meet the goals of DARPA’s Advanced Soldier Sensor Information System and Technology (ASSIST) program. As a whole, SAS provides an integrated solution that includes on-body data capture, automatic recognition of soldier activity, and a multimedia interface that combines data search and exploration. The recognition component analyzes readings from six on-body accelerometers to identify activity. The activities are modeled by boosted 1D classifiers, which allows efficient selection of the most useful features within the learning algorithm. We present empirical results based on data collected at Georgia Tech and at the Army’s Aberdeen Proving Grounds during official testing by a DARPA appointed NIST evaluation team. Our approach achieves 78.7% for continuous event recognition and 70.3% frame level accuracy. The accuracy increases to 90.3% and 90.3% respectively when considering only the modeled activities. In addition to standard error metrics, we discuss error division diagrams (EDDs) for several Aberdeen data sequences to provide a rich visual representation of the performance of our system. Keywords— activity recognition, machine learning, distributed sensors