以测量帕金森病患者姿势稳定性为目标的移动嵌入式加速计和陀螺仪可靠性测试

Matthew Thelen, Alexis Meeker, Fardeen Mazumder, Mariam Tabbah, Linda Zhu, Charlotte Tang, Nathaniel S. Miller
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引用次数: 0

摘要

帕金森病(PD)是美国第二大最常见的神经退行性疾病。帕金森病的主要症状是震颤、僵硬、运动迟缓和平衡受损。这些症状通常会干扰帕金森病患者(PwPD)的日常活动,并对生活质量(QoL)产生负面影响。因此,监测帕金森病症状对于临床评估和调整药物以帮助维持帕金森病患者的生活质量至关重要。我们正在开发一款手机应用程序,用于在家监测帕金森病症状,以便更及时、更频繁、更准确地测量帕金森病症状。虽然手机应用中收集的震颤和手指敲击结果已在以前的出版物中讨论过,但本文重点讨论应用中姿势稳定性测试的设计和评估,以及验证智能手机中嵌入式加速度计和陀螺仪的可靠性。在测试过程中,我们使用了振动器来提供振幅和频率范围与人体姿势稳定性信号相似的振动,并对加速度计和陀螺仪的测量结果进行了评估。我们使用信号处理算法来提取姿势稳定性因子,如加速度的均方根值、加速度导数、频率因子等,以及角速度的范围和均方根值。我们的研究结果表明,智能手机设备在多次试验和设备之间具有良好的一致性,从多个数据点获得的运动模式对于姿势稳定性分析是可靠的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reliability Test of Mobile Embedded Accelerometers and Gyroscopes with the Goal of Measuring Postural Stability for People with Parkinson's Disease
Parkinson's Disease (PD) is the second most common neurodegenerative disease in the United States. The cardinal symptoms of PD are tremor, rigidity, slowed movement, and impaired balance. These symptoms often interfere with the daily activities of people with Parkinson's (PwPD) and negatively affect quality of life (QoL). Therefore, monitoring PD symptoms is essential for clinical evaluations and adjusting medication to help maintain QoL for PwPD. We are developing a mobile app to conduct at-home PD symptom monitoring to provide more timely, frequent, and accurate measurements of PD symptoms. While the tremor and finger-tapping results collected in the mobile app have been discussed in previous publications, this paper focuses on the design and evaluation of postural stability tests in the app and validating the reliability of the embedded accelerometers and gyroscopes in smartphones. During the test, a shaker was employed to provide vibration in amplitude and frequency ranges similar to human postural stability signals, and both the accelerometer and gyroscope measurements were evaluated. We used signal processing algorithms to extract postural stability factors, such as the root mean square (RMS) value, the derivative of acceleration, frequency factors, etc. for the accelerations, and the ranges and RMS for the angular velocity. Our findings show that smartphone devices have good consistency over multiple trials and between devices, and motion patterns achieved from multiple data points are reliable for postural stability analysis.
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