  Human-machine interaction has been around for several decades now, with new
applications emerging every day. One of the major goals that remain to be
achieved is designing an interaction similar to how a human interacts with
another human. Therefore, there is a need to develop interactive systems that
could replicate a more realistic and easier human-machine interaction. On the
other hand, developers and researchers need to be aware of state-of-the-art
methodologies being used to achieve this goal. We present this survey to
provide researchers with state-of-the-art data fusion technologies implemented
using multiple inputs to accomplish a task in the robotic application domain.
Moreover, the input data modalities are broadly classified into uni-modal and
multi-modal systems and their application in myriad industries, including the
health care industry, which contributes to the medical industry's future
development. It will help the professionals to examine patients using different
modalities. The multi-modal systems are differentiated by a combination of
inputs used as a single input, e.g., gestures, voice, sensor, and haptic
feedback. All these inputs may or may not be fused, which provides another
classification of multi-modal systems. The survey concludes with a summary of
technologies in use for multi-modal systems.
