This study extends the previous research in which it has been shown that a mixed reality (MR) system fosters social interaction behaviours (SIBs) in children with Autism Spectrum Condition (ASC). When comparing this system to a LEGO-based non-digital intervention, it has been observed that an MR system effectively mediates a face-to-face play session between a child with ASC and a child without ASC providing new specific advantageous properties (e.g. not being a passive tool, not needing to be guided by the therapist). Considering the newly collected multimodal data totaling to 72 children (36 trials of dyads, child with ASC/child without ASC), a first goal of the present study is to apply detailed statistical inference and machine learning techniques to extensively evaluate the overall effect of this MR system, when compared to the LEGO condition. This goal also includes the analysis of psychophysiological data and allows the context-driven triangulation of the multimodal data which is operationalized by (i) video-coding of SIBs, (ii) psychophysiological data, and (iii) system logs of user-system events. A second goal is to show how SIBs, taking place in these experiences, are influenced by the internal states of the users and the system. SIBs were measured by video-coding overt behaviours (Initiation, Response and Externalization) and with self-reports. Internal states were measured using a wearable device designed by the FuBIntLab (Full-Body Interaction Lab) to acquire: Electrocardiogram (ECG) and Electrodermal Activity (EDA) data. Affective sliders and State Trait Anxiety Scale questionnaires were used as self-reports. Repeated-measures design was chosen with two conditions, the MR environment and the traditional therapy LEGO. The results show that the MR system has a positive effect on SIBs when compared to the LEGO condition, with an added advantage of being more flexible.

这项研究扩展了之前的研究，其中表明混合现实 (MR) 系统可以促进自闭症谱系 (ASC) 儿童的社交互动行为 (SIB)。当将此系统与基于乐高的非数字干预进行比较时，已经观察到 MR 系统有效地调解了有 ASC 的孩子和没有 ASC 的孩子之间的面对面游戏会话，提供了新的特定有利特性（例如，没有作为一种被动工具，不需要治疗师的指导）。考虑到新收集的总计 72 名儿童的多模态数据（36 次二元组试验，有 ASC 的儿童/无 ASC 的儿童），本研究的第一个目标是应用详细的统计推断和机器学习技术来广泛评估该研究的整体效果MR 系统，与乐高条件相比。该目标还包括心理生理学数据的分析，并允许对多模态数据进行上下文驱动的三角测量，该三角测量可通过 (i) SIB 的视频编码、(ii) 心理生理学数据和 (iii) 用户系统事件的系统日志进行操作. 第二个目标是展示在这些体验中发生的 SIB 如何受到用户和系统内部状态的影响。通过视频编码公开行为（启动、响应和外化）和自我报告来衡量 SIB。使用由 FuBIntLab（全身交互实验室）设计的可穿戴设备测量内部状态，以获取：心电图 (ECG) 和皮电活动 (EDA) 数据。情感滑块和状态特质焦虑量表问卷用作自我报告。在两个条件下选择了重复测量设计，MR 环境和传统疗法乐高。结果表明，与乐高条件相比，MR 系统对 SIB 具有积极影响，并具有更灵活的附加优势。