  Automated monitoring of construction operations, especially operations of
equipment and machines, is an essential step toward cost-estimating, and
planning of construction projects. In recent years, a number of methods were
suggested for recognizing activities of construction equipment. These methods
are based on processing single types of data (audio, visual, or kinematic
data). Considering the complexity of construction jobsites, using one source of
data is not reliable enough to cover all conditions and scenarios. To address
the issue, we utilized a data fusion approach: This approach is based on
collecting audio and kinematic data, and includes the following steps: 1)
recording audio and kinematic data generated by machines, 2) preprocessing
data, 3) extracting time- and frequency-domain-features, 4) feature-fusion, and
5) categorizing activities using a machine-learning algorithm. The proposed
approach was implemented on multiple machines and the experiments show that it
is possible to get up to 25% more-accurate results compared to cases of using
single-data-sources.
