Hori Koyu, Mao Yufeng, Ono Yumi, Ora Hiroki, Hirobe Yuki, Sawada Hiroyuki, Inaba Akira, Orimo Satoshi, Miyake Yoshihiro
School of Computing, Tokyo Institute of Technology, Yokohama, Japan.
Department of Neurology, Kanto Central Hospital, Tokyo, Japan.
Front Physiol. 2020 Jan 10;10:1530. doi: 10.3389/fphys.2019.01530. eCollection 2019.
Gait analysis is used widely in clinical practice to evaluate abnormal gait caused by disease. Conventionally, medical professionals use motion capture systems or make visual observations to evaluate a patient's gait. Recent biomedical engineering studies have proposed easy-to-use gait analysis methods employing wearable sensors with inertial measurement units (IMUs). IMUs placed on the shanks just above the ankles allow for long-term gait monitoring because the participant can walk with or without shoes during the analysis. To the knowledge of the authors, no IMU-based gait analysis method has been reported that estimates stride length, gait speed, stride duration, stance duration, and swing duration simultaneously. In the present study, we tested a proposed gait analysis method that uses IMUs attached on the shanks to estimate foot trajectory and temporal gait parameters. Our proposed method comprises two steps: stepwise dissociation of continuous gait data into multiple steps and three-dimensional trajectory estimation from data obtained from accelerometers and gyroscopes. We evaluated this proposed method by analyzing the gait of 19 able-bodied participants (mean age 23.9 years, 9 men and 10 women). Wearable sensors were attached on the participants' shanks, and we measured three-axis acceleration and three-axis angular velocity with the sensors to estimate foot trajectory during walking. We compared gait parameters estimated from the foot trajectory obtained with the proposed method and those measured with a motion capture system. Mean accuracy (± standard deviation) was 0.054 ± 0.031 m for stride length, 0.034 ± 0.039 m/s for gait speed, 0.002 ± 0.020 s for stride duration, 0.000 ± 0.017 s for stance duration, and 0.002 ± 0.024 s for swing duration. These results suggest that the proposed method is suitable for gait analysis, whereas there is a room for improvement of its accuracy and further development of this IMU-based gait analysis method will enable us to use such systems for clinical gait analysis.
步态分析在临床实践中被广泛用于评估由疾病引起的异常步态。传统上，医学专业人员使用运动捕捉系统或进行视觉观察来评估患者的步态。最近的生物医学工程研究提出了使用带有惯性测量单元（IMU）的可穿戴传感器的易于使用的步态分析方法。放置在脚踝上方小腿上的IMU允许进行长期步态监测，因为参与者在分析过程中可以穿鞋或不穿鞋行走。据作者所知，尚未有基于IMU的步态分析方法被报道能够同时估计步长、步态速度、步幅持续时间、站立持续时间和摆动持续时间。在本研究中，我们测试了一种提出的步态分析方法，该方法使用附着在小腿上的IMU来估计足部轨迹和时间步态参数。我们提出的方法包括两个步骤：将连续步态数据逐步分解为多个步骤，以及根据加速度计和陀螺仪获得的数据进行三维轨迹估计。我们通过分析19名身体健全的参与者（平均年龄23.9岁，9名男性和10名女性）的步态来评估这种提出的方法。可穿戴传感器附着在参与者的小腿上，我们使用传感器测量三轴加速度和三轴角速度，以估计行走过程中的足部轨迹。我们将用提出的方法获得的足部轨迹估计的步态参数与用运动捕捉系统测量的步态参数进行了比较。步长平均准确度（±标准差）为0.054±0.031米，步态速度为0.034±0.039米/秒，步幅持续时间为0.002±0.020秒，站立持续时间为0.000±0.017秒，摆动持续时间为0.002±0.024秒。这些结果表明，提出的方法适用于步态分析，但其准确度仍有提高空间，基于IMU的步态分析方法的进一步发展将使我们能够将此类系统用于临床步态分析。
