基于低功耗可穿戴IMU体域网络的帕金森病精确运动功能监测

W. Chang, K. Liou, Yo-Tsen Liu, K. Wen
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摘要

惯性测量单元(IMU)已广泛用于精密运动分析和评估,并在许多疾病的诊断和治疗中得到应用。帕金森病(PD)是最常见的神经退行性运动障碍,以静止性震颤、运动迟缓和僵硬为主要运动表现。本文推导了一种新的算法系统,用于检测统一帕金森病评定量表(UPDRS)的所有运动检查,并通过高速摄像系统验证了其准确性。该系统包括三大类检测参数:弹道参数、时频参数、角度参数。IMU检测的平均准确率分别可达87%、90%和95%。通过17例患者的试验测试,观察到患者的运动参数与20岁青年对照有一定的差异。对于3.6旋前和旋后,正常控制的旋转速度可以是患者的两倍,与正常控制的5度相比,振幅的偏差可以达到患者的45度。此外,为了满足低功耗和可穿戴的要求,该处理系统采用芯片解决方案设计,并采用台积电0.18 mm CMOS工艺实现。功耗为0.3713mW,芯片面积为4.2mm × 4.2mm,非常适合可穿戴应用。结果表明,该处理系统能够精确测量运动速度和振幅衰减的时间模式,并成功捕获PD患者运动迟缓和协调性差的严重程度和差异。
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Precise Motor Function Monitor for Parkinson Disease using Low Power and Wearable IMU Body Area Network
The Inertial Measurement Unit (IMU) has been widely used in precision movement analysis and evaluation and applied in the diagnosis and treatment of many diseases. Parkinson disease (PD) is the most common neurodegenerative movement disorder with rest tremor, bradykinesia, and rigidity as the cardinal motor manifestations. A novel algorithm system has been derived to detect all the motor examinations of the Unified Parkinson's Disease Rating Scale (UPDRS), of which the accuracy has been verified by high-speed camera system. This system includes three categories of detection parameters: the trajectory parameters, time-frequency parameters, angle parameters. Average accuracy for the detection with IMU can reach to 87%, 90% and 95%, respectively.With 17 patients’ trial tests, it’s observed that there do have certain differences of the movement parameters in between patients and age 20th youth controls. For 3.6 Pronation and Supination, the rotation speed of normal control can be twice of the patients and the deviation of the amplitude can reach to 45 degrees of patient in comparison to 5 degrees of normal control. Also, for low power and wearable requirements, this processing system have been designed with chip solution and be implemented with TSMC 0.18 mm CMOS process. The power consumption is 0.3713mW and the chip area is 4.2mm by 4.2mm which will be well suited to wearable applications.Our results showed that this processing system could precisely measure the temporal patterns of speed and amplitude decay of the movements, and successfully capture the severity and difference of bradykinesia and poor coordination of the patients of PD.
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