Rosenberg Michael, Thornton Ashleigh L, Lay Brendan S, Ward Brodie, Nathan David, Hunt Daniel, Braham Rebecca
School of Sport Science, Exercise and Health, University of Western Australia, M408 35 Stirling Highway, Crawley, WA, Australia 6009.
PLoS One. 2016 Jul 21;11(7):e0159356. doi: 10.1371/journal.pone.0159356. eCollection 2016.
While it has been established that using full body motion to play active video games results in increased levels of energy expenditure, there is little information on the classification of human movement during active video game play in relationship to fundamental movement skills. The aim of this study was to validate software utilising Kinect sensor motion capture technology to recognise fundamental movement skills (FMS), during active video game play. Two human assessors rated jumping and side-stepping and these assessments were compared to the Kinect Action Recognition Tool (KART), to establish a level of agreement and determine the number of movements completed during five minutes of active video game play, for 43 children (m = 12 years 7 months ± 1 year 6 months). During five minutes of active video game play, inter-rater reliability, when examining the two human raters, was found to be higher for the jump (r = 0.94, p < .01) than the sidestep (r = 0.87, p < .01), although both were excellent. Excellent reliability was also found between human raters and the KART system for the jump (r = 0.84, p, .01) and moderate reliability for sidestep (r = 0.6983, p < .01) during game play, demonstrating that both humans and KART had higher agreement for jumps than sidesteps in the game play condition. The results of the study provide confidence that the Kinect sensor can be used to count the number of jumps and sidestep during five minutes of active video game play with a similar level of accuracy as human raters. However, in contrast to humans, the KART system required a fraction of the time to analyse and tabulate the results.
虽然已经确定使用全身运动来玩主动式电子游戏会导致能量消耗水平增加，但关于在主动式电子游戏过程中人类运动与基本运动技能相关的分类信息却很少。本研究的目的是验证利用Kinect传感器运动捕捉技术的软件，以识别在主动式电子游戏过程中的基本运动技能（FMS）。两名人类评估员对跳跃和侧步进行了评分，并将这些评估结果与Kinect动作识别工具（KART）进行比较，以确定一致性水平，并确定43名儿童（平均年龄 = 12岁7个月 ± 1岁6个月）在五分钟的主动式电子游戏过程中完成的动作数量。在五分钟的主动式电子游戏过程中，当检查两名人类评估员时，发现跳跃的评分者间信度（r = 0.94，p <.01）高于侧步（r = 0.87，p <.01），尽管两者都非常好。在游戏过程中，人类评估员与KART系统之间对于跳跃的信度也非常高（r = 0.84，p <.01），而对于侧步的信度为中等（r = 0.6983，p <.01），这表明在游戏条件下，人类和KART对于跳跃的一致性高于侧步。研究结果表明，Kinect传感器可用于计算五分钟主动式电子游戏过程中的跳跃和侧步数量，其准确性与人类评估员相似。然而，与人类不同的是，KART系统分析和整理结果所需的时间仅为一小部分。
