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本文引用的文献

1
Evaluation of hand motion capture protocol using static computed tomography images: application to an instrumented glove.使用静态计算机断层扫描图像评估手部运动捕捉协议:在仪器手套中的应用。
J Biomech Eng. 2014 Dec;136(12):124501. doi: 10.1115/1.4028521.
2
An introductory study of common grasps used by adults during performance of activities of daily living.一项关于成年人在日常生活活动中常用抓握方式的初步研究。
J Hand Ther. 2014 Jul-Sep;27(3):225-33; quiz 234. doi: 10.1016/j.jht.2014.04.002. Epub 2014 Apr 21.
3
Grasp frequency and usage in daily household and machine shop tasks.掌握日常家庭和机械车间任务中的频率和用法。
IEEE Trans Haptics. 2013 Jul-Sep;6(3):296-308. doi: 10.1109/TOH.2013.6.
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Assessment of hand kinematics using inertial and magnetic sensors.使用惯性和磁力传感器评估手部运动学。
J Neuroeng Rehabil. 2014 Apr 21;11:70. doi: 10.1186/1743-0003-11-70.
5
Validity of a simple videogrammetric method to measure the movement of all hand segments for clinical purposes.一种用于临床目的测量手部所有节段运动的简单视频测量方法的有效性。
Proc Inst Mech Eng H. 2014 Feb;228(2):182-9. doi: 10.1177/0954411914522023. Epub 2014 Feb 6.
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Dataglove measurement of joint angles in sign language handshapes.数据手套对手语手型中关节角度的测量。
Sign Lang Linguist. 2012;15(1):39-72. doi: 10.1075/sll.15.1.03ecc.
7
A method for defining carpometacarpal joint kinematics from three-dimensional rotations of the metacarpal bones captured in vivo using computed tomography.一种通过使用计算机断层扫描(CT)在体内捕获的掌骨三维旋转来定义腕掌关节运动学的方法。
J Biomech. 2013 Aug 9;46(12):2104-8. doi: 10.1016/j.jbiomech.2013.05.019. Epub 2013 Jul 1.
8
Markerless motion capture and measurement of hand kinematics: validation and application to home-based upper limb rehabilitation.无标记运动捕捉和手部运动学测量:在家中进行上肢康复的验证和应用。
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9
Grasp modelling with a biomechanical model of the hand.利用手部生物力学模型进行抓握建模。
Comput Methods Biomech Biomed Engin. 2014;17(4):297-310. doi: 10.1080/10255842.2012.682156. Epub 2012 May 15.
10
Design and evaluation of a low-cost instrumented glove for hand function assessment.低成本手部功能评估仪器化手套的设计与评估。
J Neuroeng Rehabil. 2012 Jan 17;9:2. doi: 10.1186/1743-0003-9-2.

用于临床目的的测量手部运动的仪器手套的跨受试者校准。

Across-subject calibration of an instrumented glove to measure hand movement for clinical purposes.

作者信息

Gracia-Ibáñez Verónica, Vergara Margarita, Buffi James H, Murray Wendy M, Sancho-Bru Joaquín L

机构信息

a Department of Mechanical Engineering and Construction , Universitat Jaume I, Castelló , Spain.

b Department of Biomedical Engineering , Physical Medicine and Rehabilitation, and Physical Therapy and Human Movement Sciences, Northwestern University , Chicago , IL , USA.

出版信息

Comput Methods Biomech Biomed Engin. 2017 May;20(6):587-597. doi: 10.1080/10255842.2016.1265950. Epub 2016 Dec 27.

DOI:10.1080/10255842.2016.1265950
PMID:28024426
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8178967/
Abstract

Motion capture of all degrees of freedom of the hand collected during performance of daily living activities remains challenging. Instrumented gloves are an attractive option because of their higher ease of use. However, subject-specific calibration of gloves is lengthy and has limitations for individuals with disabilities. Here, a calibration procedure is presented, consisting in the recording of just a simple hand position so as to allow capture of the kinematics of 16 hand joints during daily life activities even in case of severe injured hands. 'across-subject gains' were obtained by averaging the gains obtained from a detailed subject-specific calibration involving 44 registrations that was repeated three times on multiple days to 6 subjects. In additional 4 subjects, joint angles that resulted from applying the 'across-subject calibration' or the subject-specific calibration were compared. Global errors associated with the 'across-subject calibration' relative to the detailed, subject-specific protocol were small (bias: 0.49°; precision: 4.45°) and comparable to those that resulted from repeating the detailed protocol with the same subject on multiple days (0.36°; 3.50°). Furthermore, in one subject, performance of the 'across-subject calibration' was directly compared to another fast calibration method, expressed relative to a videogrammetric protocol as a gold-standard, yielding better results.

摘要

在日常生活活动中对手部所有自由度进行运动捕捉仍然具有挑战性。仪器手套因其更高的易用性而成为一个有吸引力的选择。然而,手套的个体特定校准过程冗长,并且对残疾个体存在局限性。在此,提出了一种校准程序,该程序仅记录一个简单的手部位置,以便即使在手部严重受伤的情况下,也能在日常生活活动中捕捉16个手部关节的运动学信息。“跨个体增益”是通过对涉及44次配准的详细个体特定校准所获得的增益进行平均得到的,该校准在多天内对6名受试者重复进行了三次。在另外4名受试者中,比较了应用“跨个体校准”或个体特定校准所得到的关节角度。相对于详细的个体特定方案,与“跨个体校准”相关的全局误差较小(偏差:0.49°;精度:4.45°),并且与在多天内对同一受试者重复详细方案所得到的误差(0.36°;3.50°)相当。此外,在一名受试者中,将“跨个体校准”的性能与另一种快速校准方法直接进行了比较,相对于作为金标准的视频测量方案来表示,结果更好。