用社交机器人精准评估老人衰弱与跌倒风险，结果媲美专业医师。
Assessing Physical Frailty and Fall-Risk Indicators with Social Robots: An in situ Evaluation with Older Adults
- 机器人通过行为树协调感知与交互，自动执行标准化测试。
- 测试时间与步态参数与医生评估高度一致（ICC > 0.9）。
- 适合医疗康复机构用于日常衰弱筛查，提升评估客观性。
衰弱评估对识别老年人不良事件风险及照护需求至关重要，但传统方法依赖耗时的临床指标，如任务完成时间，常忽略生物力学信号。为此，我们提出一种由社交机器人执行的评估框架，可引导老年人完成标准化衰弱与跌倒风险测试，并采集临床评分及额外指标。系统采用行为树架构，整合感知、决策、交互与测量模块，利用视觉骨骼追踪技术评估短身体功能量表（SPPB）和起立行走测试（TUG）。该框架与医护人员共同设计，在康复中心研究室开展为期六个月的实地评估，共纳入81名老年人。机器人数据与治疗师评估及临床参考工具（包括步态分析走道和惯性测量单元，IMU）对比显示：多数任务完成时间与步态参数一致性极佳（ICC > 0.9）；SPPB总分在机器人与治疗师间具强一致性（k = 0.67），与IMU间为中等一致性（k = 0.55）。结果表明，社交机器人可在真实医疗环境中提供可靠、客观的衰弱评估，并捕捉超越传统指标的移动能力信息。
原文摘要 · Abstract (English)
Frailty assessments are crucial to evaluate the risk of adverse events and the health and social care needs of older adults, yet their administration remains resource-intensive and typically relies on coarse clinical outcomes, such as task completion times, which may overlook biomechanical indicators of functional decline. To address this, we present a robotic framework that guides older adults through standardised frailty and fall-risk tests while capturing clinical scores and additional frailty-related metrics, offering a deeper insight into a user's condition. The system uses a Behaviour Tree architecture that coordinates perception, decision-making, interaction, and measurement modules. Using vision-based skeleton tracking, the robot evaluates established clinical tests, including the Short Physical Performance Battery (SPPB) and the Timed Up and Go (TUG). The framework was co-designed with healthcare professionals and evaluated in situ during six months in a rehabilitation centre's research lab with N=81 older adults. Robot-derived measurements were compared against therapist assessments and clinical reference instruments, including a gait analysis walkway and an inertial measurement unit (IMU). Results showed excellent agreement for most test completion times and gait-related parameters ($ICC > 0.9$). And, substantial agreement for the overall SPPB score comparing the robot and the therapist ($k = 0.67$) and moderate agreement comparing the robot and the IMU ($k=0.55$). The findings highlight that social robots can provide reliable and objective frailty assessments in healthcare settings while enabling the collection of relevant mobility indicators beyond conventional outcomes.
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