使用一个飞行时间相机在跑步机上行走的质心(CoM)运动和脚的位置。

IF 3.5 3区 综合性期刊 Q2 CHEMISTRY, ANALYTICAL
Sensors Pub Date : 2025-09-19 DOI:10.3390/s25185850
Joshua T Chang, Alisha Ragatz, Anjana Ganesh, Ana P Quiros Padilla, Mikayla R Devins, Christina V Mihova, John G Milton
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引用次数: 0

摘要

在繁忙的临床环境中评估患者的跌倒风险是具有挑战性的。由于空间的限制,诸如计时-出发测试和窄梁行走等测试很难进行。此外,将这些测试结果直接与基本的步态稳定性生物力学原理联系起来并不容易,这些原理强调身体质心(CoM)和支撑基础(BoS)运动之间的相互作用。在这里,我们展示了一个1.2米长的跑步机和一个单一的“飞行时间”Azure Kinect摄像头可以在5分钟内捕捉CoM-BoS的相互作用。CoM是通过Kinect相机的身体跟踪SDK测量的20个关节位置将身体分成14个部分来计算的。通过跟踪每一步的关节和关节位置,我们可以使用无标记、无接触、节省空间的方法来评估不同的步态稳定性指标。一个与足部位置相关的大型数字数据库将有助于未来统计和机器学习技术的发展,以识别摔倒风险较高的受试者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Center of Mass (CoM) Motions and Foot Placement During Treadmill Walking Using One Time-of-Flight Camera.

Center of Mass (CoM) Motions and Foot Placement During Treadmill Walking Using One Time-of-Flight Camera.

Center of Mass (CoM) Motions and Foot Placement During Treadmill Walking Using One Time-of-Flight Camera.

Center of Mass (CoM) Motions and Foot Placement During Treadmill Walking Using One Time-of-Flight Camera.

Assessing the fall risk of a patient in a busy clinical setting is challenging. Tests such as the timed-up-and-go test and narrow beam walking are difficult to perform due to space restrictions. Moreover, it is not easy to directly connect the results of these tests to fundamental biomechanical principles of gait stability, which emphasize the interplay between the movements of the body's center of mass (CoM) and its base of support (BoS). Herein, we show how a 1.2 m-long treadmill and a single "time-of-flight" Azure Kinect camera can capture the CoM-BoS interplay within 5 min. The CoM was calculated by dividing the body into 14 segments determined from 20 joint positions measured by the Kinect camera's body tracking SDK. By tracking the CoM and joint positions from stride to stride, we can evaluate different gait stability metrics using a markerless, contactless, space-efficient approach. A large digital database of CoM movements relative to foot placement will be useful for the future development of statistical and machine learning techniques for identifying subjects at higher risk of falling.

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来源期刊
Sensors
Sensors 工程技术-电化学
CiteScore
7.30
自引率
12.80%
发文量
8430
审稿时长
1.7 months
期刊介绍: Sensors (ISSN 1424-8220) provides an advanced forum for the science and technology of sensors and biosensors. It publishes reviews (including comprehensive reviews on the complete sensors products), regular research papers and short notes. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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