Fusco N, Crétual A
University Rennes 2, ENS Cachan, UFR APS, Avenue Charles Tillon, CS 24 414, Rennes, Bretagne, France.
Gait Posture. 2008 Nov;28(4):663-7. doi: 10.1016/j.gaitpost.2008.04.016. Epub 2008 Jun 20.
The treadmill is frequently used in research and clinical assessments for gait analysis. To evaluate mechanical energy and dynamics of walking, the fluctuations of the treadmill speed have to be taken into account. A new algorithm is presented in this study to determine instantaneous treadmill speed using solely the kinematic data of subjects. The algorithm uses an automatic detection of heel contact (HC) and toe off (TO) during treadmill walking. Kinematic data were collected from two groups (healthy adult, n=11; hemiplegic adult, n=9). The gait events determination is validated by comparison with two visual inspection methods. Our algorithm is able to determine instantaneous treadmill speed with accuracy. In fact, the root mean square error between the computed speed (CS) and the measure speed (MS) was weak with an average value of 0.04+/-0.021ms(-1). So, the computed speed reflects the variations of the belt speed and could be an important contribution to energetic and dynamic gait analysis on a treadmill.
跑步机常用于步态分析的研究和临床评估。为了评估步行的机械能和动力学,必须考虑跑步机速度的波动。本研究提出了一种新算法,仅使用受试者的运动学数据来确定跑步机的瞬时速度。该算法利用自动检测跑步机行走过程中的足跟触地(HC)和足趾离地(TO)。从两组(健康成年人,n = 11;偏瘫成年人,n = 9)收集运动学数据。通过与两种视觉检查方法进行比较,验证了步态事件的判定。我们的算法能够准确地确定跑步机的瞬时速度。事实上,计算速度(CS)与测量速度(MS)之间的均方根误差很小,平均值为0.04±0.021m·s⁻¹。因此,计算速度反映了皮带速度的变化,可能对跑步机上的能量和动态步态分析有重要贡献。