  Worker monitoring and protection in collaborative robot (cobots) industrial
environments requires advanced sensing capabilities and flexible solutions to
monitor the movements of the operator in close proximity of moving robots.
Collaborative robotics is an active research area where Internet of Things
(IoT) and novel sensing technologies are expected to play a critical role.
Considering that no single technology can currently solve the problem of
continuous worker monitoring, the paper targets the development of an IoT
multisensor data fusion (MDF) platform. It is based on an edge-cloud
architecture that supports the combination and transformation of multiple
sensing technologies to enable the passive and anonymous detection of workers.
Multidimensional data acquisition from different IoT sources, signal
pre-processing, feature extraction, data distribution and fusion, along with
machine learning (ML) and computing methods are described. The proposed IoT
platform also comprises a practical solution for data fusion and analytics. It
is able to perform opportunistic and real-time perception of workers by fusing
and analyzing radio signals obtained from several interconnected IoT
components, namely a multi-antenna WiFi installation (2.4-5 GHz), a sub-THz
imaging camera (100 GHz), a network of radars (122 GHz) and infrared sensors
(8-13 {\mu}m). The performance of the proposed IoT platform is validated
through real use case scenarios inside a pilot industrial plant in which
protective human--robot distance must be guaranteed considering latency and
detection uncertainties.
