文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期,新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2025

第一代和第二代Microsoft Kinect™在识别静态姿势时关节中心位置方面的有效性。

The validity of the first and second generation Microsoft Kinect™ for identifying joint center locations during static postures.

作者信息

Xu Xu, McGorry Raymond W

机构信息

Liberty Mutual Research Institute for Safety, 71 Frankland Road, Hopkinton, MA 01748, USA.

Liberty Mutual Research Institute for Safety, 71 Frankland Road, Hopkinton, MA 01748, USA.

出版信息

Appl Ergon. 2015 Jul;49:47-54. doi: 10.1016/j.apergo.2015.01.005. Epub 2015 Feb 17.


DOI:10.1016/j.apergo.2015.01.005
PMID:25766422
Abstract

The Kinect™ sensor released by Microsoft is a low-cost, portable, and marker-less motion tracking system for the video game industry. Since the first generation Kinect sensor was released in 2010, many studies have been conducted to examine the validity of this sensor when used to measure body movement in different research areas. In 2014, Microsoft released the computer-used second generation Kinect sensor with a better resolution for the depth sensor. However, very few studies have performed a direct comparison between all the Kinect sensor-identified joint center locations and their corresponding motion tracking system-identified counterparts, the result of which may provide some insight into the error of the Kinect-identified segment length, joint angles, as well as the feasibility of adapting inverse dynamics to Kinect-identified joint centers. The purpose of the current study is to first propose a method to align the coordinate system of the Kinect sensor with respect to the global coordinate system of a motion tracking system, and then to examine the accuracy of the Kinect sensor-identified coordinates of joint locations during 8 standing and 8 sitting postures of daily activities. The results indicate the proposed alignment method can effectively align the Kinect sensor with respect to the motion tracking system. The accuracy level of the Kinect-identified joint center location is posture-dependent and joint-dependent. For upright standing posture, the average error across all the participants and all Kinect-identified joint centers is 76 mm and 87 mm for the first and second generation Kinect sensor, respectively. In general, standing postures can be identified with better accuracy than sitting postures, and the identification accuracy of the joints of the upper extremities is better than for the lower extremities. This result may provide some information regarding the feasibility of using the Kinect sensor in future studies.

摘要

微软发布的Kinect™传感器是一款面向视频游戏行业的低成本、便携式且无需标记的运动跟踪系统。自2010年第一代Kinect传感器发布以来,已经开展了许多研究来检验该传感器在不同研究领域用于测量身体运动时的有效性。2014年,微软发布了供计算机使用的第二代Kinect传感器,其深度传感器具有更高的分辨率。然而,很少有研究对所有Kinect传感器识别的关节中心位置与其相应的运动跟踪系统识别的对应位置进行直接比较,其结果可能会为Kinect识别的节段长度、关节角度的误差以及将逆动力学应用于Kinect识别的关节中心的可行性提供一些见解。本研究的目的是首先提出一种将Kinect传感器的坐标系相对于运动跟踪系统的全局坐标系进行对齐的方法,然后检验在日常活动的8种站立姿势和8种坐姿期间Kinect传感器识别的关节位置坐标的准确性。结果表明,所提出的对齐方法可以有效地将Kinect传感器相对于运动跟踪系统进行对齐。Kinect识别的关节中心位置的准确程度取决于姿势和关节。对于直立站立姿势,第一代和第二代Kinect传感器在所有参与者和所有Kinect识别的关节中心上的平均误差分别为76毫米和87毫米。一般来说,站立姿势的识别准确性优于坐姿,并且上肢关节的识别准确性优于下肢关节。这一结果可能会为未来研究中使用Kinect传感器的可行性提供一些信息。

相似文献

[1]
The validity of the first and second generation Microsoft Kinect™ for identifying joint center locations during static postures.

Appl Ergon. 2015-7

[2]
Validity of the Microsoft Kinect for measurement of neck angle: comparison with electrogoniometry.

Int J Occup Saf Ergon. 2017-12

[3]
Validation of a Kinect V2 based rehabilitation game.

PLoS One. 2018-8-24

[4]
Comparative abilities of Microsoft Kinect and Vicon 3D motion capture for gait analysis.

J Med Eng Technol. 2014-7

[5]
Using the Microsoft Kinect™ to assess 3-D shoulder kinematics during computer use.

Appl Ergon. 2017-4-7

[6]
Development of a robust and cost-effective 3D respiratory motion monitoring system using the kinect device: Accuracy comparison with the conventional stereovision navigation system.

Comput Methods Programs Biomed. 2018-3-30

[7]
Keeping up with video game technology: objective analysis of Xbox Kinect™ and PlayStation 3 Move™ for use in burn rehabilitation.

Burns. 2014-8

[8]
Accuracy evaluation of the Kinect v2 sensor during dynamic movements in a rehabilitation scenario.

Annu Int Conf IEEE Eng Med Biol Soc. 2016-8

[9]
Accuracy of the Microsoft Kinect for measuring gait parameters during treadmill walking.

Gait Posture. 2015-7

[10]
Real-time posture reconstruction for Microsoft Kinect.

IEEE Trans Cybern. 2013-8-22

引用本文的文献

[1]
Comparative analysis of Microsoft Kinect Azure and manual measurement methods in the sit and reach test among elite female weightlifters.

Sci Rep. 2025-7-9

[2]
Exploring Trends and Clusters in Human Posture Recognition Research: An Analysis Using CiteSpace.

Sensors (Basel). 2025-1-22

[3]
Autonomous modeling of repetitive movement for rehabilitation exercise monitoring.

BMC Med Inform Decis Mak. 2022-7-3

[4]
Validity and Reliability of Kinect v2 for Quantifying Upper Body Kinematics during Seated Reaching.

Sensors (Basel). 2022-4-2

[5]
Kinect v2-Assisted Semi-Automated Method to Assess Upper Limb Motor Performance in Children.

Sensors (Basel). 2022-3-15

[6]
The Reliability of the Microsoft Kinect and Ambulatory Sensor-Based Motion Tracking Devices to Measure Shoulder Range-of-Motion: A Systematic Review and Meta-Analysis.

Sensors (Basel). 2021-12-8

[7]
Automating the Clinical Assessment of Independent Wheelchair Sitting Pivot Transfer Techniques.

Top Spinal Cord Inj Rehabil. 2021

[8]
Instrumental Validity of the Motion Detection Accuracy of a Smartphone-Based Training Game.

Int J Environ Res Public Health. 2021-8-9

[9]
Simple benchmarking method for determining the accuracy of depth cameras in body landmark location estimation: Static upright posture as a measurement example.

PLoS One. 2021

[10]
Detection of Postural Control in Young and Elderly Adults Using Deep and Machine Learning Methods with Joint-Node Plots.

Sensors (Basel). 2021-5-5

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索