设计、开发与评估一种用于监测和指导中风后康复训练的交互式个性化社交机器人
Design, Development, and Evaluation of an Interactive Personalized Social Robot to Monitor and Coach Post-Stroke Rehabilitation Exercises
AI总结：
本研究设计并评估了一种集成神经网络与规则模型的社交机器人系统，用于个性化监测和指导中风后康复锻炼，通过个体数据调整实现实时反馈，性能与专家水平相当。
摘要：
社交辅助机器人正越来越多地被探索用于提高老年人和残疾人在健康及福祉相关锻炼中的参与度。然而，即使人们具有各种不同的身体状况，先前大多数关于社交机器人锻炼指导系统的研究都采用了通用的、预定义的反馈。这些系统的部署仍然是一个挑战。在本文中，我们介绍了我们与治疗师和中风后幸存者进行迭代合作，以设计、开发和评估一种用于个性化康复的社交机器人锻炼指导系统的工作。通过采访治疗师，我们设计了该系统与用户交互的方式，然后开发了一个交互式社交机器人锻炼指导系统。该系统集成了一个神经网络模型和一个基于规则的模型，以自动监测和评估患者的康复锻炼，并可根据个体患者的数据进行调整，以生成实时的、个性化的纠正性反馈以促进改善。利用来自15名中风后幸存者的康复锻炼数据集，我们证明了我们的系统在利用留出患者数据进行调整时，其评估患者锻炼的性能显著提高。此外，我们的现实世界评估研究表明，我们的系统能够适应新的参与者，并达到了0.81的平均评估性能，这与专家的共识水平相当。我们进一步讨论了我们的系统在实践中的潜在益处和局限性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
