Until the present days, there has been little advances in the relation between the shop-floor operator in an industrial environment and the machines execution the manufacturing processes. Normally, the semi-automatic processes for collaborative assembly in industry are composed of a human and non-human elements. In the human perspective, one or more persons can be working in the same cell directly or indirectly with a non-human entity. In a cell can exist several machines, normally robotic arms that perform very specific collaborative tasks with the operators. However, the latest advances are mostly related with security issues and regulations, like immediately stopping the machine if a human touches it, and not much related with operative issues like adjusting the process velocity (within a certain window of cycle time) or give preference to some tasks over another in the beginning of the shift, to benefit the operator's working conditions. Therefore, a step forward to a more advanced interaction between machine and operator should be taken, towards a more adaptive and rich symbiosis. The main goal of the present Dissertation is to explore the relation between the shop-floor operator and the machine in a cyber physical system. For that purpose, biometric sensors will be used (ECG, EMG, EDA, PZT, wearables and others) to monitor the operators physiology during the operative times, and based on that, explore how a collaborative process can be adapted to minimize the operator's stress and fatigue. First, the correct set of sensors should be explored to understand how stress and fatigue metrics can be calculated. Secondly, optimization techniques need to be studied in order to, e.g. finds the correct machine's process parameterization that, on one hand, minimizes the operator's fatigue and stress, and on the other, do not jeopardizes the process requirements in terms of timing and quality. Therefore, this can be stated as a multivariate optimization problem
