用时间序列大模型预测缺失动作，让机器人上肢评估从5分钟缩至1分钟。
Reducing Robotic Upper-Limb Assessment Time While Maintaining Precision: A Time Series Foundation Model Approach
- 用Chronos等时序大模型预测未完成的运动轨迹，补足初始8或16次试次
- 仅需8次真实记录+模型预测，即可达到24-28次完整测试的可靠性（ICC≥0.90）
- 特别适合中风患者，显著降低疲劳负担，适合临床快速筛查
目的：在Kinarm机器人上进行视觉引导伸手测试（VGR）可生成敏感的运动学生物标志物，但需完成40-64次伸手，耗时且易致疲劳。本文评估时间序列基础模型是否能替代早期试次中的未记录动作，同时保持标准Kinarm参数的可靠性。方法：分析461名中风患者与599名健康对照者在4目标和8目标伸手任务中的速度信号。仅保留前8或16次真实试次，利用ARIMA、MOMENT和Chronos模型，在70%受试者数据上微调后预测合成试次。将真实与预测试次合并，重新计算反应时间、运动时间、姿态速度、最大速度四项运动学特征，并以组内相关系数ICC(2,1)与完整测试结果对比。结果：Chronos模型在仅8次真实试次基础上，使所有参数的ICC≥0.90，等效于24-28次真实试次的可靠性（ΔICC≤0.07）；MOMENT表现居中，ARIMA提升有限。各人群与任务下，合成试次均有效替代缺失动作，未明显损害特征可靠性。结论：基于基础模型的预测可大幅缩短Kinarm VGR评估时间。对最严重中风患者，测试时长从4-5分钟降至约1分钟，仍保持运动学精度。该预测增强范式为中风后运动功能评估提供了高效解决方案。
原文摘要 · Abstract (English)
Purpose: Visually Guided Reaching (VGR) on the Kinarm robot yields sensitive kinematic biomarkers but requires 40-64 reaches, imposing time and fatigue burdens. We evaluate whether time-series foundation models can replace unrecorded trials from an early subset of reaches while preserving the reliability of standard Kinarm parameters. Methods: We analyzed VGR speed signals from 461 stroke and 599 control participants across 4- and 8-target reaching protocols. We withheld all but the first 8 or 16 reaching trials and used ARIMA, MOMENT, and Chronos models, fine-tuned on 70 percent of subjects, to forecast synthetic trials. We recomputed four kinematic features of reaching (reaction time, movement time, posture speed, maximum speed) on combined recorded plus forecasted trials and compared them to full-length references using ICC(2,1). Results: Chronos forecasts restored ICC >= 0.90 for all parameters with only 8 recorded trials plus forecasts, matching the reliability of 24-28 recorded reaches (Delta ICC <= 0.07). MOMENT yielded intermediate gains, while ARIMA improvements were minimal. Across cohorts and protocols, synthetic trials replaced reaches without materially compromising feature reliability. Conclusion: Foundation-model forecasting can greatly shorten Kinarm VGR assessment time. For the most impaired stroke survivors, sessions drop from 4-5 minutes to about 1 minute while preserving kinematic precision. This forecast-augmented paradigm promises efficient robotic evaluations for assessing motor impairments following stroke.
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