An Impact Study of the Design of Exergaming Parameters on Body Intensity from Objective and Gameplay-Based Player Experience Perspectives, Based on Balance Training Exergame

Abstract

Kinect-based exergames allow players to undertake physical exercise in an interactive manner with visual stimulation. Previous studies focused on investigating physical fitness based on calories or heart rate to ascertain the effectiveness of exergames. However, designing an exergame for specific training purposes, with intensity levels suited to the needs and skills of the players, requires the investigation of motion performance to study player experience.This study investigates how parameters of a Kinect-based exergame, combined with balance training exercises, influence the balance control ability and intensity level the player can tolerate, by analyzing both objective and gameplay-based player experience, and taking enjoyment and difficulty levels into account.The exergame tested required participants to maintain their balance standing on one leg within a posture frame (PF) while a force plate evaluated the player's balance control ability in both static and dynamic gaming modes. The number of collisions with the PF depended on the frame's travel time for static PFs, and the leg-raising rate and angle for dynamic PFs. In terms of center of pressure (COP) metrics, significant impacts were caused by the frame's travel time on MDIST-AP for static PFs, and the leg-raising rate on MDIST-ML and TOTEX for dynamic PFs. The best static PF balance control performance was observed with a larger frame offset by a travel time of 2 seconds, and the worst performance with a smaller frame and a travel time of 1 second. The best dynamic PF performance was with a leg-raising rate of 1 second at a 45-degree angle, while the worst performance was with a rate of 2 seconds at a 90-degree angle.The results demonstrated that different evaluation methods for player experience could result in different findings, making it harder to study the design of those exergames with training purposes based on player experience.

Multi-Scale Entropy Analysis of Body Sway for Investigating Balance Ability During Exergame Play Under Different Parameter Settings

“…Of the static posture frame parameters, manipulation of posture frame travel time was found to cause significant variation in both MSE-AP and MSE-ML. These results are consistent with those obtained by Sun et al [28] for mean distance-anterior posterior (MDIST-AP) COP measurements (see the Appendix for more details). Results for the dynamic posture frame are shown in Table 3.…”

“…It was found that for the dynamic posture frame, MSE-AP varied significantly for different values of leg-raising rates. Of the dynamic posture frame parameters, manipulation of leg-raising angle and leg-raising rate was found to cause significant variation in MSE-AP, which is also consistent with the results of previous work on COP indicators by Sun et al [28], in which mean distance-medial lateral (MDIST-ML) and total excursions (TOTEX) both manifested significant differences (see Appendix). With respect to parameter design and difficulty level, the results of the complexity analysis indicate that the posture frame travel time and leg-raising rate parameters affect the ability of the subjects to adapt.…”

“…With respect to physical performance, in light of the results of studies based on traditional indicator analysis [28] and multi-scale entropy, our hope was to find the optimal parameter settings using multi-scale entropy analysis to analyze physical performance and to use this information to guide the design of the game. To successfully train the user, an exergame presents a particular set of body movements as a game.…”

“…In our previous study analyzing traditional balance indicators, the parameters of a Kinect-based exergame were combined with balance training exercises, which influenced the balance control ability and intensity level that players could tolerate [28]. The goal of the present study is to flexibly modify the exergame so that it can be tailored to the needs of various individuals using the same set of parameters.…”

“…(3) Compare the results of traditional indicator analysis [28] to those of multi-scale entropy analysis, to determine the viability of multi-scale entropy analysis as a tool for human-centered design, or as another assessment in the future.…”