Psychomotor Profiling with Bayesian Neworks -Prediction of User Abilities based on Inputs of Motorized Wheelchair Parameters

A high level of psychomotor abilities often is essential for successfully operating many technical systems and especially medical and rehabilitation devices. This paper introduces an approach to enable a technical system to automatically assess its user’s level of psychomotor abilities, so that it can adapt its level of automation and provide the user with more or less assistance depending on the individual user profile. For this purpose, a study has been conducted during which the motor abilities of the participants have been assessed and their wheelchair control behavior recorded. Bayesian Networks (BN) and Structural Equation Models (SEM) have been applied to model the relationships between the wheelchair control behavior and the motor abilities of the participants. The BN demonstrate usefulness and magnificent advantages compared to the SEM for modeling uncertainty in structure and parameter dependencies, which are shown by validation experiments. Although only a small amount of data samples (23 participants) was available for model generation, a target variable reflecting the user's precision ability was successfully classified based on real data input in more than 82% of cases. Keywords—Human-Technology Interaction, Bayesian Networks, Psychomotor Abilities, Adaptive Automation