  Touchless computer interaction has become an important consideration during
the COVID-19 pandemic period. Despite progress in machine learning and computer
vision that allows for advanced gesture recognition, an integrated collection
of such open-source methods and a user-customisable approach to utilising them
in a low-cost solution for touchless interaction in existing software is still
missing. In this paper, we introduce the MotionInput v2.0 application. This
application utilises published open-source libraries and additional gesture
definitions developed to take the video stream from a standard RGB webcam as
input. It then maps human motion gestures to input operations for existing
applications and games. The user can choose their own preferred way of
interacting from a series of motion types, including single and bi-modal hand
gesturing, full-body repetitive or extremities-based exercises, head and facial
movements, eye tracking, and combinations of the above. We also introduce a
series of bespoke gesture recognition classifications as DirectInput triggers,
including gestures for idle states, auto calibration, depth capture from a 2D
RGB webcam stream and tracking of facial motions such as mouth motions,
winking, and head direction with rotation. Three use case areas assisted the
development of the modules: creativity software, office and clinical software,
and gaming software. A collection of open-source libraries has been integrated
and provide a layer of modular gesture mapping on top of existing mouse and
keyboard controls in Windows via DirectX. With ease of access to webcams
integrated into most laptops and desktop computers, touchless computing becomes
more available with MotionInput v2.0, in a federated and locally processed
method.
