Pham Minh H, Warmerdam Elke, Elshehabi Morad, Schlenstedt Christian, Bergeest Lu-Marie, Heller Maren, Haertner Linda, Ferreira Joaquim J, Berg Daniela, Schmidt Gerhard, Hansen Clint, Maetzler Walter
Department of Neurology, University Hospital Schleswig-Holstein, Kiel University, Kiel, Germany.
Digital Signal Processing and System Theory, Faculty of Engineering, Kiel University, Kiel, Germany.
Front Neurol. 2018 Aug 10;9:652. doi: 10.3389/fneur.2018.00652. eCollection 2018.
Impaired sit-to-stand and stand-to-sit movements (postural transitions, PTs) in patients with Parkinson's disease (PD) and older adults (OA) are associated with risk of falling and reduced quality of life. Inertial measurement units (IMUs, also called "wearables") are powerful tools to monitor PT kinematics. The purpose of this study was to develop and validate an algorithm, based on a single IMU positioned at the lower back, for PT detection and description in the above-mentioned groups in a home-like environment. Four PD patients (two with dyskinesia) and one OA served as algorithm training group, and 21 PD patients (16 without and 5 with dyskinesia) and 11 OA served as test group. All wore an IMU on the lower back and were videotaped while performing everyday activities for 90-180 min in a non-standardized home-like environment. Accelerometer and gyroscope signals were analyzed using discrete wavelet transformation (DWT), a six degrees-of-freedom (DOF) fusion algorithm and vertical displacement estimation. From the test group, 1,001 PTs, defined by video reference, were analyzed. The accuracy of the algorithm for the detection of PTs against video observation was 82% for PD patients without dyskinesia, 47% for PD patients with dyskinesia and 85% for OA. The overall accuracy of the PT direction detection was comparable across groups and yielded 98%. Mean PT duration values were 1.96 s for PD patients and 1.74 s for OA based on the algorithm ( < 0.001) and 1.77 s for PD patients and 1.51 s for OA based on clinical observation ( < 0.001). Validation of the PT detection algorithm in a home-like environment shows acceptable accuracy against the video reference in PD patients without dyskinesia and controls. Current limitations are the PT detection in PD patients with dyskinesia and the use of video observation as the video reference. Potential reasons are discussed.
帕金森病(PD)患者和老年人(OA)从坐到站和从站到坐动作(姿势转换,PTs)受损与跌倒风险及生活质量下降相关。惯性测量单元(IMUs,也称为“可穿戴设备”)是监测PT运动学的有力工具。本研究的目的是开发并验证一种基于置于下背部的单个IMU的算法,用于在类似家庭的环境中对上述群体的PT进行检测和描述。4名PD患者(2名有运动障碍)和1名OA作为算法训练组,21名PD患者(16名无运动障碍和5名有运动障碍)和11名OA作为测试组。所有人在下背部佩戴IMU,并在非标准化的类似家庭的环境中进行90 - 180分钟日常活动时被录像。使用离散小波变换(DWT)、六自由度(DOF)融合算法和垂直位移估计对加速度计和陀螺仪信号进行分析。从测试组中,分析了由视频参考定义的1001次PT。该算法针对视频观察检测PTs的准确率,无运动障碍的PD患者为82%,有运动障碍的PD患者为47%,OA为85%。PT方向检测的总体准确率在各组间相当,为98%。基于算法,PD患者的平均PT持续时间值为1.96秒,OA为1.74秒(<0.001);基于临床观察,PD患者为1.77秒,OA为1.51秒(<0.001)。在类似家庭的环境中对PT检测算法的验证表明,相对于无运动障碍的PD患者和对照组的视频参考,其准确率可接受。当前的局限性在于有运动障碍的PD患者的PT检测以及将视频观察用作视频参考。对潜在原因进行了讨论。