Stroke patients with upper limb motor impairments are re-acclimated to their corresponding motor functionalities through therapeutic interventions. Physiotherapists typically assess these functionalities using various qualitative protocols. However, such assessments are often biased and prone to errors, reducing rehabilitation efficacy. Therefore, real-time visualization and quantitative analysis of performance metrics, such as range of motion, repetition rate, velocity, etc., are crucial for accurate progress assessment. This study introduces Renovo, a working prototype of a wearable motion sensor-based assistive technology that assists physiotherapists with real-time visualization of these metrics. We also propose a novel mathematical framework for generating quantitative performance scores without relying on any machine learning model. We present the results of a three-week pilot study involving 16 stroke patients with upper limb disabilities, evaluated across three successive sessions at one-week intervals by both Renovo and physiotherapists (N=5). Results suggest that while the expertise of a physiotherapist is irreplaceable, Renovo can assist in the decision-making process by providing valuable quantitative information.

翻译：上肢运动障碍的脑卒中患者通过治疗干预重新适应相应的运动功能。物理治疗师通常使用各种定性方案评估这些功能。然而，此类评估常存在主观偏差且易产生误差，从而降低康复疗效。因此，对运动范围、重复频率、速度等性能指标进行实时可视化与定量分析，对于准确评估康复进展至关重要。本研究介绍了Renovo——一种基于可穿戴运动传感器的工作原型辅助技术，可协助物理治疗师实时可视化这些指标。我们还提出了一种新颖的数学框架，用于生成定量绩效评分，且无需依赖任何机器学习模型。我们展示了一项为期三周的试点研究结果，该研究涉及16名上肢残疾的脑卒中患者，由Renovo和物理治疗师（N=5）以一周为间隔连续三次进行评估。结果表明，虽然物理治疗师的专业经验不可替代，但Renovo可通过提供有价值的定量信息辅助决策过程。