Pareto-based Dynamic Difficulty Adjustment of a Competitive Exergame for Arm Rehabilitation (Preprint)

BACKGROUND Lack of motivation is a major hindrance to frequent and intense exercise which is critical to rehabilitating people with arm disabilities due to old age, neurological disorders or stroke. Recently, the use of interpersonal exergames has been associated with increased motivation and exercise intensity in arm rehabilitation and is becoming a common trend. However, the Dynamic Difficulty Adjustment (DDA) of such games is still an open issue because unlike the traditional DDA frameworks where game intensity is simply adapted to suit the players' performance, the aim of DDA for exergames is to optimize the conflicting objectives namely of intensity and performance. Objective: To design a dedicated DDA for rehabilitation exergames that optimize the conflicting objectives of intensity and performance by generating a set of feasible trade-off solutions. Based on the rehabilitative needs, the tradeoff worth information of each solution is to be used to select a unique solution. OBJECTIVE To design a dedicated DDA for rehabilitation exergames that optimizes the conflicting objectives of intensity and performance by generating a set of feasible trade-off solutions. Based on the rehabilitative needs, the tradeoff worth information of each solution is to be used to select a unique solution. METHODS We designed a Pareto-based DDA for a competitive exergame that optimizes the two conflicting objectives. Using a set of feasible solutions generated during the first episode of the game, the proposed Pareto-based DDA is used to modify the parameters of the game. Optimizing conflicting objectives generally results in a set of trade-off solutions called Pareto optimal set instead of a single solution. Therefore, the DDA is capable of selecting a single solution from the optimal Pareto based on the trade-off worth information of each solution in the optimal Pareto set. RESULTS Results: Experimental results with 12 unimpaired participants show the capability of the proposed Pareto-based DDA to online adjust the game parameters effectively based on a trade-off between the intensity and motivation. CONCLUSIONS Since rehabilitation outcomes rely on both intensity and motivation, unlike traditional DDA approaches, the capability of Pareto-based DDA to provide trade-off solutions between conflicting objectives of intensity and motivation is very promising to rehabilitation outcomes. However, multi-session investigation over a period of time needs to be carried out to examine if they influence rehabilitation outcomes positively. CLINICALTRIAL This work is not a clinical trial. Although humans participated in this study, they participate in the evaluation of a single-session of a rehabilitation exergame rather than a comprehensive rehabilitation intervention with no health outcomes investigated.