Supervised machine-learning algorithms accurately modeled user engagement in long-term, in-home socially assistive robot interventions. Socially assistive robotics (SAR) has great potential to provide accessible, affordable, and personalized therapeutic interventions for children with autism spectrum disorders (ASD). However, human-robot interaction (HRI) methods are still limited in their ability to autonomously recognize and respond to behavioral cues, especially in atypical users and everyday settings. This work applies supervised machine-learning algorithms to model user engagement in the context of long-term, in-home SAR interventions for children with ASD. Specifically, we present two types of engagement models for each user: (i) generalized models trained on data from different users and (ii) individualized models trained on an early subset of the user’s data. The models achieved about 90% accuracy (AUROC) for post hoc binary classification of engagement, despite the high variance in data observed across users, sessions, and engagement states. Moreover, temporal patterns in model predictions could be used to reliably initiate reengagement actions at appropriate times. These results validate the feasibility and challenges of recognition and response to user disengagement in long-term, real-world HRI settings. The contributions of this work also inform the design of engaging and personalized HRI, especially for the ASD community.

中文翻译：

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对自闭症谱系障碍儿童进行长期，家庭内社交辅助机器人干预的参与度建模

受监督的机器学习算法可以在长期的家庭内部社交辅助机器人干预中准确地模拟用户参与度。社会辅助机器人技术（SAR）在为自闭症谱系障碍（ASD）儿童提供可访问，可负担且个性化的治疗干预措施方面具有巨大潜力。但是，人机交互（HRI）方法在自动识别和响应行为提示的能力方面仍然受到限制，特别是在非典型用户和日常环境中。这项工作应用了受监督的机器学习算法来在ASD儿童的长期，在家SAR干预中模拟用户参与度。具体来说，我们为每个用户提供两种类型的参与度模型：（i）对来自不同用户的数据进行训练的通用模型，以及（ii）对用户数据的早期子集进行训练的个性化模型。尽管在用户，会话和参与状态之间观察到的数据差异很大，但是该模型对参与的事后二进制分类实现了约90％的准确性（AUROC）。此外，模型预测中的时间模式可用于在适当的时间可靠地启动重新接合动作。这些结果验证了在长期，真实世界的HRI环境中识别和响应用户脱离接触的可行性和挑战。这项工作的贡献还有助于设计出引人入胜且个性化的HRI，特别是对于ASD社区而言。尽管跨用户，会话和参与状态观察到的数据差异很大。此外，模型预测中的时间模式可用于在适当的时间可靠地启动重新接合动作。这些结果验证了在长期，真实世界的HRI环境中识别和响应用户脱离接触的可行性和挑战。这项工作的贡献还有助于设计出引人入胜且个性化的HRI，特别是对于ASD社区而言。尽管跨用户，会话和参与状态观察到的数据差异很大。此外，模型预测中的时间模式可用于在适当的时间可靠地启动重新接合动作。这些结果验证了在长期，真实世界的HRI环境中识别和响应用户脱离接触的可行性和挑战。这项工作的贡献还有助于设计出引人入胜且个性化的HRI，特别是对于ASD社区而言。