Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy.
Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, 86100 Campobasso, Italy.
Sensors (Basel). 2022 Aug 23;22(17):6341. doi: 10.3390/s22176341.
The detection analysis of neurodegenerative diseases by means of low-cost sensors and suitable classification algorithms is a key part of the widely spreading telemedicine techniques. The choice of suitable sensors and the tuning of analysis algorithms require a large amount of data, which could be derived from a large experimental measurement campaign involving voluntary patients. This process requires a prior approval phase for the processing and the use of sensitive data in order to respect patient privacy and ethical aspects. To obtain clearance from an ethics committee, it is necessary to submit a protocol describing tests and wait for approval, which can take place after a typical period of six months. An alternative consists of structuring, implementing, validating, and adopting a software simulator at most for the initial stage of the research. To this end, the paper proposes the development, validation, and usage of a software simulator able to generate movement disorders-related data, for both healthy and pathological conditions, based on raw inertial measurement data, and give tri-axial acceleration and angular velocity as output. To present a possible operating scenario of the developed software, this work focuses on a specific case study, i.e., the Parkinson's disease-related tremor, one of the main disorders of the homonym pathology. The full framework is reported, from raw data availability to pathological data generation, along with a common machine learning method implementation to evaluate data suitability to be distinguished and classified. Due to the development of a flexible and easy-to-use simulator, the paper also analyses and discusses the data quality, described with typical measurement features, as a metric to allow accurate classification under a low-performance sensing device. The simulator's validation results show a correlation coefficient greater than 0.94 for angular velocity and 0.93 regarding acceleration data. Classification performance on Parkinson's disease tremor was greater than 98% in the best test conditions.
利用低成本传感器和合适的分类算法对神经退行性疾病进行检测分析是广泛应用的远程医疗技术的关键部分。选择合适的传感器和调整分析算法需要大量的数据，这些数据可以从涉及自愿患者的大型实验测量活动中获得。这个过程需要一个预先批准的阶段，用于处理和使用敏感数据，以尊重患者的隐私和伦理方面。为了获得伦理委员会的批准，需要提交一份描述测试的协议，并等待批准，通常需要六个月的时间。另一种选择是构建、实现、验证和采用软件模拟器，最多在研究的初始阶段使用。为此，本文提出了一种软件模拟器的开发、验证和使用，该模拟器能够基于原始惯性测量数据生成与运动障碍相关的数据，包括健康和病理条件，并输出三轴加速度和角速度。为了展示所开发软件的可能操作场景，本工作重点研究了一个特定的案例研究，即帕金森病相关震颤，这是同名义病理的主要障碍之一。报告了完整的框架，从原始数据的可用性到病理数据的生成，以及实施常见的机器学习方法来评估数据的可区分性和可分类性。由于开发了灵活易用的模拟器，本文还分析和讨论了数据质量，使用典型的测量特征进行描述，作为在低性能感测设备下进行准确分类的指标。模拟器的验证结果表明，角速度的相关系数大于 0.94，加速度数据的相关系数大于 0.93。在最佳测试条件下，帕金森病震颤的分类性能大于 98%。
