IEEE J Biomed Health Inform. 2021 Jan;25(1):3-12. doi: 10.1109/JBHI.2020.2982978. Epub 2021 Jan 5.
Inertial measurement units (IMU) have been used for gait analysis in many clinical studies, as a more convenient, low cost and less restricted alternative to the laboratory-based motion capture systems or instrumented walkways. Spatial-temporal gait parameters such as gait cycle duration and stride length calculated from the IMUs were often used in these studies for evaluating the impaired gait. However, the spatial-temporal information provided by IMUs is limited, and sometime suffers incomplete and less effective evaluation. In this study, we develop a novel IMU-based method for clinical gait evaluation. Nine gait variables including three spatial-temporal parameters and six kinematic parameters are extracted from two shank-mounted IMUs for quantifying patient's gait deviations. Based on those parameters, an IMU-based gait normalcy index (INI) is derived to evaluate the overall gait performance. Eight inpatient subjects with gait impairments caused by n-hexane neuropathy and ten healthy subjects were recruited. The proposed gait variables and INI were examined on the inpatients at three to five time instants during the rehabilitation process until being discharged. A comparison with healthy subjects and statistical analysis for the changes of gait variables and INI demonstrated that the proposed new set of gait variables and INI can provide adequate and effective information for quantifying gait abnormalities, and help understanding the progress of gait and effectiveness of therapy during rehabilitation process.
惯性测量单元(IMU)已在许多临床研究中用于步态分析,作为基于实验室的运动捕捉系统或仪器化步道的更方便、低成本和限制较少的替代方案。这些研究中常常用从 IMU 计算出的步态周期持续时间和步长等时空步态参数来评估受损的步态。然而,IMU 提供的时空信息有限,有时会受到不完全和效果不佳的评估。在这项研究中,我们开发了一种基于 IMU 的新型临床步态评估方法。从两个安装在小腿上的 IMU 中提取了九个步态变量,包括三个时空参数和六个运动学参数,用于量化患者的步态偏差。基于这些参数,推导出基于 IMU 的步态正常指数(INI)来评估整体步态性能。招募了 8 名因正己烷神经病导致步态障碍的住院患者和 10 名健康受试者。在康复过程中,对住院患者在三个到五个时间点进行了所提出的步态变量和 INI 的检查,直到出院。与健康受试者的比较和对步态变量和 INI 变化的统计分析表明,新提出的步态变量和 INI 可以提供充分有效的信息来量化步态异常,并有助于理解康复过程中步态的进展和治疗效果。