用关节轨迹建模患者康复路径，精准评估恢复进展。
Individualised recovery trajectories of patients with impeded mobility, using distance between probability distributions of learnt graphs
- 基于20个关节位置时序数据构建贝叶斯随机几何图，计算康复评分差异。
- 通过概率分布距离量化每次训练的恢复变化，绘制个性化康复轨迹。
- 适合康复医学研究者与个性化治疗方案设计者参考。
在重症后物理康复过程中，患者需依赖可靠的累计表现反馈。本文提出一种个体化康复轨迹学习方法：利用患者执行特定运动时20个关节的位置时序数据，在电子平台记录下，通过贝叶斯学习构建随机几何图，计算其后验概率分布间的统计距离，从而得出每次训练的运动恢复评分（MRS）。该方法可精确刻画患者在重复练习中的恢复动态，进而生成个性化康复轨迹。我们推导出图中任意边的闭式边缘后验分布，基于相关结构的多变量时间序列数据。在此基础上，为不同运动功能障碍水平的患者提供最优训练方案建议。
原文摘要 · Abstract (English)
Patients who are undergoing physical rehabilitation, benefit from feedback that follows from reliable assessment of their cumulative performance attained at a given time. In this paper, we provide a method for the learning of the recovery trajectory of an individual patient, as they undertake exercises as part of their physical therapy towards recovery of their loss of movement ability, following a critical illness. The difference between the Movement Recovery Scores (MRSs) attained by a patient, when undertaking a given exercise routine on successive instances, is given by a statistical distance/divergence between the (posterior) probabilities of random graphs that are Bayesianly learnt using time series data on locations of 20 of the patient's joints, recorded on an e-platform as the patient exercises. This allows for the computation of the MRS on every occasion the patient undertakes this exercise, using which, the recovery trajectory is drawn. We learn each graph as a Random Geometric Graph drawn in a probabilistic metric space, and identify the closed-form marginal posterior of any edge of the graph, given the correlation structure of the multivariate time series data on joint locations. On the basis of our recovery learning, we offer recommendations on the optimal exercise routines for patients with given level of mobility impairment.
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