Human-robot interaction is a growing field that aims to research and develop communication channels between humans and robots to enhance comfort, safety, and productivity in healthcare, the household, and the industry. Researchers have considered ergonomy-related metrics to compose these channels for physical human-robot collaborative scenarios. We refer to these communication channels as arbitration methods. Several of these metrics, such as human arm manipulability and muscle fatigue, have taken their turns in the literature to set the base for arbitration methods reaching promising results. Human arm force manipulability represents the transmission between joint torques in the joint space and end-point force in the task space depending on the configuration of the joint angles. Muscle fatigue keeps track of the muscle activation and builds up depending on the muscle activation level and previous fatigue value. The first one has predictive value. The other has a reactive value.\par
Nevertheless, no work in the literature explores the power of combining both metrics into an arbitration method. Here we develop a multi-metric arbitration method that combines human arm force manipulability and muscle fatigue as input for a finite state machine (FSM) that translates the human multi-metric state to robot control level over a collaborative task. Although some modifications may be worth trying and evaluating to reach generalizability in physical human-robot collaborative tasks, the system reached satisfactory results. Moreover, as future steps, we should conduct human-factors research to compare the effect of the system on task performance.