Socially assistive robots are increasingly being explored to improve the engagement of older adults and people with disability in health and well-being-related exercises. However, even if people have various physical conditions, most prior work on social robot exercise coaching systems has utilized generic, predefined feedback. The deployment of these systems still remains a challenge. In this paper, we present our work of iteratively engaging therapists and post-stroke survivors to design, develop, and evaluate a social robot exercise coaching system for personalized rehabilitation. Through interviews with therapists, we designed how this system interacts with the user and then developed an interactive social robot exercise coaching system. This system integrates a neural network model with a rule-based model to automatically monitor and assess patients' rehabilitation exercises and can be tuned with individual patient's data to generate real-time, personalized corrective feedback for improvement. With the dataset of rehabilitation exercises from 15 post-stroke survivors, we demonstrated our system significantly improves its performance to assess patients' exercises while tuning with held-out patient's data. In addition, our real-world evaluation study showed that our system can adapt to new participants and achieved 0.81 average performance to assess their exercises, which is comparable to the experts' agreement level. We further discuss the potential benefits and limitations of our system in practice.

翻译：社交辅助机器人正被越来越多地探索用于提升老年人和残障人士在健康及福祉相关锻炼中的参与度。然而，尽管人们具有不同的身体状况，先前大多数关于社交机器人运动指导系统的研究仍使用了通用的、预设的反馈机制。这些系统的部署仍面临挑战。本文介绍了我们通过迭代式地与治疗师和中风幸存者共同参与，设计、开发并评估了一种用于个性化康复的社交机器人运动指导系统。通过与治疗师的访谈，我们设计了该系统与用户的交互方式，进而开发了一个交互式社交机器人运动指导系统。该系统集成了神经网络模型与基于规则的模型，可自动监测和评估患者的康复锻炼情况，并能根据个体患者数据进行调整，以实时生成个性化纠正反馈以促进改善。利用15名中风幸存者的康复锻炼数据集，我们证明了该系统在根据未参与训练的患者数据调整后，其评估患者锻炼表现的能力显著提升。此外，我们的实际环境评估研究表明，该系统能够适应新参与者，并在评估其锻炼时达到0.81的平均性能，与专家间的一致水平相当。我们进一步讨论了该系统在实践中的潜在优势与局限。