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一种用于检测脊髓损伤后轮椅使用者主动推进动作的新型算法。

A novel algorithm for detecting active propulsion in wheelchair users following spinal cord injury.

作者信息

Popp Werner L, Brogioli Michael, Leuenberger Kaspar, Albisser Urs, Frotzler Angela, Curt Armin, Gassert Roger, Starkey Michelle L

机构信息

Rehabilitation Engineering Lab, ETH Zurich, Switzerland; Spinal Cord Injury Center, University Hospital Balgrist, Switzerland.

Spinal Cord Injury Center, University Hospital Balgrist, Switzerland.

出版信息

Med Eng Phys. 2016 Mar;38(3):267-74. doi: 10.1016/j.medengphy.2015.12.011. Epub 2016 Feb 8.

Abstract

Physical activity in wheelchair-bound individuals can be assessed by monitoring their mobility as this is one of the most intense upper extremity activities they perform. Current accelerometer-based approaches for describing wheelchair mobility do not distinguish between self- and attendant-propulsion and hence may overestimate total physical activity. The aim of this study was to develop and validate an inertial measurement unit based algorithm to monitor wheel kinematics and the type of wheelchair propulsion (self- or attendant-) within a "real-world" situation. Different sensor set-ups were investigated, ranging from a high precision set-up including four sensor modules with a relatively short measurement duration of 24 h, to a less precise set-up with only one module attached at the wheel exceeding one week of measurement because the gyroscope of the sensor was turned off. The "high-precision" algorithm distinguished self- and attendant-propulsion with accuracy greater than 93% whilst the long-term measurement set-up showed an accuracy of 82%. The estimation accuracy of kinematic parameters was greater than 97% for both set-ups. The possibility of having different sensor set-ups allows the use of the inertial measurement units as high precision tools for researchers as well as unobtrusive and simple tools for manual wheelchair users.

摘要

对于坐轮椅的人,身体活动可以通过监测其移动性来评估,因为这是他们进行的最剧烈的上肢活动之一。目前基于加速度计描述轮椅移动性的方法无法区分自行驱动和他人推动,因此可能高估总体身体活动量。本研究的目的是开发并验证一种基于惯性测量单元的算法,以在“现实世界”情境中监测车轮运动学以及轮椅推进类型(自行或他人)。研究了不同的传感器设置,从高精度设置(包括四个传感器模块,测量持续时间相对较短,为24小时)到不太精确的设置(仅在车轮上附着一个模块,测量时间超过一周,因为传感器的陀螺仪已关闭)。“高精度”算法区分自行驱动和他人推动的准确率超过93%,而长期测量设置的准确率为82%。两种设置下运动学参数的估计准确率均大于97%。不同传感器设置的可能性使得惯性测量单元既可以作为研究人员的高精度工具,也可以作为手动轮椅使用者的不显眼且简单的工具。

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