BACKGROUND There is a lack of early (infant) mobility rehabilitation approaches that incorporate natural and complex environments and have the potential to concurrently advance motor, cognitive, and social development. The Grounded Early Adaptive Rehabilitation (GEAR) system is a pediatric learning environment designed to provide motor interventions that are grounded in social theory and can be applied in early life. Within a perceptively complex and behaviorally natural setting, GEAR utilizes novel body-weight support technology and socially-assistive robots to both ease and encourage mobility in young children through play-based, child-robot interaction. This methodology article reports on the development and integration of the different system components and presents preliminary evidence on the feasibility of the system. METHODS GEAR consists of the physical and cyber components. The physical component includes the playground equipment to enrich the environment, an open-area body weight support (BWS) device to assist children by partially counter-acting gravity, two mobile robots to engage children into motor activity through social interaction, and a synchronized camera network to monitor the sessions. The cyber component consists of the interface to collect human movement and video data, the algorithms to identify the children's actions from the video stream, and the behavioral models for the child-robot interaction that suggest the most appropriate robot action in support of given motor training goals for the child. The feasibility of both components was assessed via preliminary testing. Three very young children (with and without Down syndrome) used the system in eight sessions within a 4-week period. RESULTS All subjects completed the 8-session protocol, participated in all tasks involving the selected objects of the enriched environment, used the BWS device and interacted with the robots in all eight sessions. Action classification algorithms to identify early child behaviors in a complex naturalistic setting were tested and validated using the video data. Decision making algorithms specific to the type of interactions seen in the GEAR system were developed to be used for robot automation. CONCLUSIONS Preliminary results from this study support the feasibility of both the physical and cyber components of the GEAR system and demonstrate its potential for use in future studies to assess the effects on the co-development of the motor, cognitive, and social systems of very young children with mobility challenges.

中文翻译：

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为儿科运动康复打造智能环境。


背景技术缺乏结合自然和复杂环境并有可能同时促进运动、认知和社会发展的早期（婴儿）活动康复方法。扎根早期适应性康复 (GEAR) 系统是一种儿科学习环境，旨在提供基于社会理论并可应用于生命早期的运动干预措施。在感知复杂且行为自然的环境中，GEAR 利用新颖的体重支撑技术和社交辅助机器人，通过基于游戏的儿童机器人互动来缓解和鼓励幼儿的活动能力。这篇方法论文章报告了不同系统组件的开发和集成，并提供了系统可行性的初步证据。 METHODS GEAR 由物理和网络组件组成。物理部分包括丰富环境的游乐场设备、通过部分反作用重力来帮助儿童的开放区域体重支撑 (BWS) 设备、两个通过社交互动让儿童参与运动活动的移动机器人以及同步摄像头网络来监视会话。网络组件包括收集人体运动和视频数据的界面、从视频流中识别儿童动作的算法以及儿童与机器人交互的行为模型，该模型建议最合适的机器人动作以支持给定的运动训练孩子的目标。通过初步测试评估了这两个组件的可行性。三个非常年幼的孩子（患有或不患有唐氏综合症）在 4 周内使用了该系统八次。 结果 所有受试者都完成了 8 个会话协议，参与了涉及丰富环境中选定对象的所有任务，使用了 BWS 设备，并在所有 8 个会话中与机器人进行了交互。使用视频数据测试和验证了在复杂的自然环境中识别早期儿童行为的动作分类算法。针对 GEAR 系统中交互类型的决策算法被开发用于机器人自动化。结论 这项研究的初步结果支持了 GEAR 系统的物理和网络组件的可行性，并证明了其在未来研究中的潜力，以评估对幼儿运动、认知和社会系统共同发展的影响行动不便的儿童。