On Using Controller Input and Signal Processing as a Parameter for Learning in CPHS

Abstract

With the emergence of safe autonomous vehicles and systems, there is a demand for creating systems that are aware of and responsive to the human. There have been decades of work dedicated to human-in-the loop studies. However, when it comes to systems that are responsive to the human based on learning parameters, there is a need for the appropriate input parameters to assess learning. In this work, signal processing methods were used to analyze game controller input signals in response to humans completing a simulated quadrotor landing task with three levels of difficulty (easy, medium, and difficult) over 30 trials. Data collected from twelve adults were analyzed using the energy of the controller input signal; 2) non-dimensional velocity; and 3) dominant frequency analysis. The landing trajectories were also mapped graphically revealing three categories of learners: beginner, intermediate, and trained. The results from the signal processing analysis procedure provided supporting evidence for these categories. The results of this work suggests that input parameters from a game controller can be used as a proxy for learning and can provide an additional means for enabling human aware systems.