Information-based human motor performance models

Despite the evolution of technologies that brought to the advent of I4.0, in current work environments, the operator still plays a crucial role in motor activities that require repetitive movements to be executed. Repetitive movements characterize a great range of motor tasks that can be performed in multiple work environments such as factories (e.g., manual assembly tasks, manual sorting), laboratories (e.g., pick and place tasks) or even outdoor (e.g., construction work manual tasks). The evaluation of motor performance by focusing on movements executed by operators or prescribed by the task has not yet considered in the current scientific literature. The present dissertation addresses the topic of motor performance by introducing information-based models relying on the Fitts’ Law Index of Difficulty (ID). The proposed models consider the entire motor behaviour (required or observed) for the correct execution of repetitive motor tasks. The topic is investigated and discussed under a new point of view, where features of the environment and individual’s abilities influence the quality of the motor behaviour (demanded or executed) and affect the motor performance. Results show the effectiveness of the models proposed, underlying the importance of the motor behaviour for the correct execution of the motor tasks. The performance is not only linked to the efficiency in achieving a task goal, but also on how physically the task goal is reached. The proposed models have a general validity, not limited to specific applications/work environments, paving the way to a novel motor performance perspective domain independent.