帕金森病(PD)的特点是各种运动和非运动症状,其中一些与步态和平衡有关。使用传感器监测患者的移动性和步态参数的提取,已成为评估其治疗效果和疾病进展的客观方法。为此,两种流行的解决方案是压力鞋垫和基于IMU的身体穿戴设备,用于精确,连续,远程,和被动步态评估。在这项工作中,评估基于鞋垫和IMU的解决方案以评估步态障碍,随后进行了比较,提供证据支持在日常临床实践中使用仪器。使用两个数据集进行评估,在临床研究期间产生的,PD患者穿着的衣服,同时,一对仪表鞋垫和一套可穿戴的基于IMU的设备。该研究的数据用于提取和比较步态特征,独立,从上述两个系统。随后,由提取的特征组成的子集,被机器学习算法用于步态障碍评估。结果表明,鞋垫步态运动学特征与从基于IMU的设备中提取的特征高度相关。此外,两者都有能力训练准确的机器学习模型来检测PD步态障碍.
Parkinson\'s disease (PD) is characterized by a variety of motor and non-motor symptoms, some of them pertaining to gait and balance. The use of sensors for the monitoring of patients\' mobility and the extraction of gait parameters, has emerged as an objective method for assessing the efficacy of their treatment and the progression of the disease. To that end, two popular solutions are pressure insoles and body-worn IMU-based devices, which have been used for precise, continuous, remote, and passive gait assessment. In this work, insole and IMU-based solutions were evaluated for assessing gait impairment, and were subsequently compared, producing evidence to support the use of instrumentation in everyday clinical practice. The evaluation was conducted using two datasets, generated during a clinical study, in which patients with PD wore, simultaneously, a pair of instrumented insoles and a set of wearable IMU-based devices. The data from the study were used to extract and compare gait features, independently, from the two aforementioned systems. Subsequently, subsets comprised of the extracted features, were used by machine learning algorithms for gait impairment assessment. The results indicated that insole gait kinematic features were highly correlated with those extracted from IMU-based devices. Moreover, both had the capacity to train accurate machine learning models for the detection of PD gait impairment.