  Visual Human Activity Recognition (HAR) and data fusion with other sensors
can help us at tracking the behavior and activity of underground miners with
little obstruction. Existing models, such as Single Shot Detector (SSD),
trained on the Common Objects in Context (COCO) dataset is used in this paper
to detect the current state of a miner, such as an injured miner vs a
non-injured miner. Tensorflow is used for the abstraction layer of implementing
machine learning algorithms, and although it uses Python to deal with nodes and
tensors, the actual algorithms run on C++ libraries, providing a good balance
between performance and speed of development. The paper further discusses
evaluation methods for determining the accuracy of the machine-learning and an
approach to increase the accuracy of the detected activity/state of people in a
mining environment, by means of data fusion.
