The use of manually fed machines (e.g. table saws) bares risks of injury that are clearly above the average level of other high risk workplaces.
The wide use of such machines causes severe problems for occupational safety and implies high costs for medical treatments and accident annuities.
This thesis presents a new concept of a multispectral sensor to monitor an area in front of a danger zone to detect the user’s limbs and trigger safeguarding measures to prevent an accident in time.
The sensor concept realizes a contact-free material classification, which comprises the development of a system design and specific safety requirements with respect to international safety standards.
Furthermore, a prototypical implementation using four wavebands, which were determined for skin detection through an analysis of reflectance spectra acquired specifically for this purpose, was built.
This sensor comprises an embedded system which is able to perform a material classification within a few milliseconds.
To achieve this, several algorithms were researched and developed to process the raw sensor readings.
An evaluation of the presented methods on both real and synthesized sensor data as well as on the prototypical implementation was performed.
The evaluation yields that the prototype implementing the presented methods can detect human skin reliably within a wide range of measurement conditions, including the presence of interference sources.