The high prevalence of work-related musculoskeletal disorders (WMSDs) could be addressed by optimizing Human-Robot Collaboration (HRC) frameworks. In this context, this paper proposes a methodology for ergonomically effective task delegation and HRC for manufacturing applications based on two hypotheses. The first hypothesis states that it is possible to rapidly quantify ergonomically professional tasks using motion data from a few wearable sensors and then delegate the highrisk tasks to a collaborative robot. The second hypothesis is that, compared to typical HRC frameworks involving physical interaction, the ergonomics and safety of an HRC scenario can be enhanced by combining gesture recognition and pose estimation. These remove unnecessary motions that could expose operators to ergonomic risks, decrease the amount of physical effort, and make the robot aware and responsive to the presence and gestures of the operators. The methodology is evaluated by optimizing the HRC scenario of a television manufacturing process, yet it is described how it can be reconfigured for other industrial scenarios. The effect of the temporal and spatial adaptation on the operator's range of motion was analyzed through three separate experiments. The effectiveness of HRC is measured through the standard key performance indicators (KPIs); however, to evaluate the collaboration and required physical demand, two KPIs are proposed in this paper. These are the rate of spatial adaptation and the rate of reduction in the operator's motion. The results demonstrated that the methodology enhanced the ergonomics and efficiency of the production process. First, the robot was delegated two tasks identified as the most ergonomically dangerous for human operators. Then the optimized HRC achieved an average rate of spatial adaptation of 29.37% and a decrease in operator movement of 28.8% across 14 subjects compared to HRC frameworks that do not include spatial and temporal adaptation.