Vienne-Jumeau Aliénor, Oudre Laurent, Moreau Albane, Quijoux Flavien, Vidal Pierre-Paul, Ricard Damien
COGNAC-G (UMR 8257), CNRS Service de Santé des Armées University Paris Descartes, 75006 Paris, France.
L2TI, University Paris 13, 93430 Villetaneuse, France.
Sensors (Basel). 2019 Jul 12;19(14):3089. doi: 10.3390/s19143089.
Gait assessment and quantification have received an increased interest in recent years. Embedded technologies and low-cost sensors can be used for the longitudinal follow-up of various populations (neurological diseases, elderly, etc.). However, the comparison of two gait trials remains a tricky question as standard gait features may prove to be insufficient in some cases. This article describes a new algorithm for comparing two gait trials recorded with inertial measurement units (IMUs). This algorithm uses a library of step templates extracted from one trial and attempts to detect similar steps in the second trial through a greedy template matching approach. The output of our method is a similarity index (SId) comprised between 0 and 1 that reflects the similarity between the patterns observed in both trials. Results on healthy and multiple sclerosis subjects show that this new comparison tool can be used for both inter-individual comparison and longitudinal follow-up.
近年来,步态评估与量化越来越受到关注。嵌入式技术和低成本传感器可用于对各类人群(神经疾病患者、老年人等)进行长期跟踪。然而,由于在某些情况下标准步态特征可能并不充分,比较两次步态试验仍然是一个棘手的问题。本文介绍了一种用于比较由惯性测量单元(IMU)记录的两次步态试验的新算法。该算法使用从一次试验中提取的步幅模板库,并试图通过贪婪模板匹配方法在第二次试验中检测相似的步幅。我们方法的输出是一个介于0和1之间的相似性指数(SId),它反映了两次试验中观察到的模式之间的相似性。对健康受试者和多发性硬化症患者的研究结果表明,这种新的比较工具可用于个体间比较和长期跟踪。