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Research Papers

Application of Digital Human Models to Physiotherapy Training

[+] Author and Article Information
Takao Kakizaki

Department of Mechanical Engineering,
Nihon University,
Nakagawara 1, Tokusada, Tamura,
Koriyama 963-8642, Fukushima, Japan
e-mail: kakizaki.takao@nihon-u.ac.jp

Mai Endo

Department of Mechanical Engineering,
Nihon University,
Nakagawara 1, Tokusada, Tamura,
Koriyama 963-8642, Fukushima, Japan
e-mail: bluefascination@gmail.com

Jiro Urii

CAS Research,
44-4-105 Shimo,
Fussa 197-0023, Tokyo, Japan
e-mail: Jiro.URII@cas.fussa.tokyo.jp

Mitsuru Endo

Department of Mechanical Engineering,
Nihon University,
Nakagawara 1, Tokusada, Tamura,
Koriyama 963-8642, Fukushima, Japan
e-mail: m_endo@mech.ce.nihon-u.ac.jp

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received December 23, 2016; final manuscript received April 27, 2017; published online July 18, 2017. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 17(3), 031014 (Jul 18, 2017) (16 pages) Paper No: JCISE-16-2157; doi: 10.1115/1.4036991 History: Received December 23, 2016; Revised April 27, 2017

The importance of physiotherapy is becoming more significant with the increasing number of countries with aging populations. Thus, the education of physiotherapists is a crucial concern in many countries. Information and communications technologies, such as motion capture systems, have been introduced to sophisticate the training methods used in physiotherapy. However, the methods employed in most training schools for physiotherapists and occupational therapists remain dependent on more conventional materials. These materials include conventional textbooks with samples of traditional gait motion photographs and video archives of patients' walking motion. Actual on-site clinical training is also utilized in current physiotherapy education programs. The present paper addresses an application of a previously developed digital human model called the kinematic digital human (KDH) to physiotherapy education with a focus on improving students' understanding of the gait motion of disabled patients. KDH models for use in physiotherapy were constructed based on Rancho Los Amigos National Rehabilitation Center (RLANRC) terminology, which is considered the preferred standard among clinicians. The developed KDH models were employed to allow the three-dimensional visualization of the gait motion of a hemiplegic patient.

Copyright © 2017 by ASME
Topics: Kinematics
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References

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Figures

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Fig. 1

Projected growth of elderly population in Japan

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Fig. 2

KDH model and its joint arrangements

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Fig. 3

Joint-centered coordinate frames in KDH model

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Fig. 4

Gait motion classification by RLANRC method

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Fig. 5

Schematic of gait motion KDH models construction

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Fig. 6

Series of normal gait motion images corresponding to initial postures of eight representative phases of RLANRC method

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Fig. 7

Gait motion KDH models for normal gait motion

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Fig. 8

Gait motion KDH model-based coordinate frames

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Fig. 9

Gait motion photographs of right hemiplegic patient [20]

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Fig. 10

Initial KDH models of right hemiplegic patient

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Fig. 11

KDH models corresponding to initial postures of eight representative phases of RLANRC method for subject with normal gait (similar to Fig. 7)

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Fig. 12

Four normal gait postures corresponding to photographs of right hemiplegic patient shown in Fig. 9

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Fig. 13

Appropriate RLANRC positions of initial KDH models of hemiplegic patient shown in Fig. 10

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Fig. 14

RLANRC KDH models of hemiplegic patient gait

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Fig. 15

Gait motion KDH models obtained by RLANRC-based modeling

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Fig. 16

Visualizations of gait motion KDH model in 3D space: (a) side view, (b) front view, (c) top view, and (d) orthographic view

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Fig. 17

Relationship between RLANRC gait phase and right hip joint angle: (a) normal gait data [23], (b) normal gait motion KDH model, and (c) hemiplegic gait motion KDH model

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Fig. 18

Relationship between RLANRC gait phase and right knee joint angle: (a) normal gait data [23], (b) normal gait motion KDH model, and (c) hemiplegic gait motion KDH model

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Fig. 19

Relationship between RLANRC gait phase and right ankle joint angle: (a) normal gait data [23], (b) normal gait motion KDH model, and (c) hemiplegic gait motion KDH model

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Fig. 20

Continuous images of gait motion represented by patient KDH model before treatment: (a) side view and (b) front view

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Fig. 21

Continuous images of gait motion represented by patient KDH model after treatment: (a) side view and (b) front view

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Fig. 22

Detail of the joint coordinate frame arrangement in the KDH model

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