Providing specialized rehabilitation and tailoring the training process for patient’s needs and according to recovery potentials has gained importance. To satisfy this need, a dynamic assessment of the performance of the recovery process is required. Assessing rehabilitation for the upper limb is often carried out with clinical subjective scales that do not satisfy these requirements. The use of technologies introduced several sensors into the devices used for rehabilitation and permitted the rise of kinematic assessments.

Kinematic measures provide an objective scale to follow up recovery during upper limb rehabilitation. The kinematics are still raw evaluations since they present insignificant effects if studied over short periods or on heterogeneous samples.

We propose a framework for modeling the trajectories as a means of encoding the specificity of the movement at every stage. The new technique permits detecting significant differences as soon as three training sessions became available.

We adopt an expectation–maximization algorithm and an optimization technique to encode the trajectories and the transition model from the acquired data. The framework enables us to encode in a Bayesian sense the observations from the patient and define six metrics to follow up on the progress of the movement quality. Statistical analysis of the results proved that these metrics are effective in tracking the evolution of the recovery. The results also established a strong discriminative property.

The proposed framework promises a finer scale of evaluation and extends the knowledge about kinematic assessment. This study’s findings suggest that adopting these new metrics can help achieve more individualized patient care. It additionally promises to limit the amount of data needed to detect a significant change.

提供专门的康复服务并根据患者的需求和康复潜力定制培训过程已变得越来越重要。为了满足此需求，需要对恢复过程的性能进行动态评估。上肢的康复评估通常使用不满足这些要求的临床主观量表进行。技术的使用在用于康复的设备中引入了多个传感器，并允许进行运动学评估。

运动学测量提供了客观的量表，以跟踪上肢康复期间的恢复情况。运动学仍然是原始的评估，因为如果在短期内或在异质样本上进行研究，它们的影响不大。

我们提出了一个对轨迹建模的框架，作为在每个阶段编码运动的特殊性的一种手段。新技术允许在三个培训课程可用后立即检测出显着差异。

我们采用期望最大化算法和优化技术来对所获取数据的轨迹和过渡模型进行编码。该框架使我们能够在贝叶斯意义上对患者的观察进行编码，并定义六个指标来跟踪运动质量的进展。结果的统计分析证明，这些指标可以有效地跟踪恢复的演变过程。结果还建立了很强的区分性。

拟议的框架有望实现更好的评估规模，并扩展有关运动学评估的知识。这项研究的发现表明，采用这些新指标可以帮助实现更个性化的患者护理。此外，它还承诺会限制检测到重大更改所需的数据量。