Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland.
Rehabilitation Engineering Laboratory, ETH Zurich, Zurich, Switzerland.
J Neuroeng Rehabil. 2023 Apr 29;20(1):55. doi: 10.1186/s12984-023-01178-9.
An incomplete spinal cord injury (SCI) refers to remaining sensorimotor function below the injury with the possibility for the patient to regain walking abilities. However, these patients often suffer from diverse gait deficits, which are not objectively assessed in the current clinical routine. Wearable inertial sensors are a promising tool to capture gait patterns objectively and started to gain ground for other neurological disorders such as stroke, multiple sclerosis, and Parkinson's disease. In this work, we present a data-driven approach to assess walking for SCI patients based on sensor-derived outcome measures. We aimed to (i) characterize their walking pattern in more depth by identifying groups with similar walking characteristics and (ii) use sensor-derived gait parameters as predictors for future walking capacity.
The dataset analyzed consisted of 66 SCI patients and 20 healthy controls performing a standardized gait test, namely the 6-min walking test (6MWT), while wearing a sparse sensor setup of one sensor attached to each ankle. A data-driven approach has been followed using statistical methods and machine learning models to identify relevant and non-redundant gait parameters.
Clustering resulted in 4 groups of patients that were compared to each other and to the healthy controls. The clusters did differ in terms of their average walking speed but also in terms of more qualitative gait parameters such as variability or parameters indicating compensatory movements. Further, using longitudinal data from a subset of patients that performed the 6MWT several times during their rehabilitation, a prediction model has been trained to estimate whether the patient's walking speed will improve significantly in the future. Including sensor-derived gait parameters as inputs for the prediction model resulted in an accuracy of 80%, which is a considerable improvement of 10% compared to using only the days since injury, the present 6MWT distance, and the days until the next 6MWT as predictors.
In summary, the work presented proves that sensor-derived gait parameters provide additional information on walking characteristics and thus are beneficial to complement clinical walking assessments of SCI patients. This work is a step towards a more deficit-oriented therapy and paves the way for better rehabilitation outcome predictions.
不完全性脊髓损伤（SCI）是指损伤以下仍存在感觉运动功能，患者有可能恢复行走能力。然而，这些患者常患有多种步态缺陷，但目前的临床常规并未对此进行客观评估。可穿戴惯性传感器是一种很有前途的工具，可以客观地捕捉步态模式，并开始在其他神经系统疾病（如中风、多发性硬化症和帕金森病）中得到应用。在这项工作中，我们提出了一种基于传感器衍生的结果测量来评估 SCI 患者行走能力的方法。我们旨在（i）通过识别具有相似行走特征的组来更深入地描述他们的行走模式，以及（ii）使用传感器衍生的步态参数作为未来行走能力的预测因子。
分析的数据集包括 66 名 SCI 患者和 20 名健康对照者，他们在穿着稀疏传感器装置（每个脚踝各贴一个传感器）的情况下进行标准化步态测试，即 6 分钟步行测试（6MWT）。使用统计方法和机器学习模型进行了数据驱动的方法，以识别相关且非冗余的步态参数。
聚类产生了 4 组患者，然后将这些组与健康对照组进行了比较。这些聚类在平均行走速度方面存在差异，但在更定性的步态参数方面也存在差异，如变异性或表示代偿运动的参数。此外，使用从一组在康复期间多次进行 6MWT 的患者中获得的纵向数据，训练了一个预测模型，以估计患者的行走速度是否会在未来显著提高。将传感器衍生的步态参数作为输入纳入预测模型，可将准确性提高到 80%，与仅使用损伤后天数、当前 6MWT 距离和下一次 6MWT 之间的天数作为预测因子相比，这是一个相当大的 10%的提高。
总之，本研究证明了传感器衍生的步态参数提供了行走特征的额外信息，因此有利于补充 SCI 患者的临床行走评估。这项工作是迈向更以缺陷为导向的治疗方法的一步，为更好地预测康复结果铺平了道路。
