Human body dynamics

Report
例3:外界センサー
人間
 モーションキャプチャ:
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運動データ
 床半力
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外力
逆運動学計算
例3:外界センサー
人間
動作画像
モーションキャプチャー
逆運動学
逆力学
モーションキャプチャー
例3:外界センサー
人間
15 リンク
[Nakamura et al. 2000] ,
[Venture et al. 2008]
34 自由度(DOF)
マーカーから逆運動学計算
→関節角度、速度、加速度 [Yamane
et al. 2003]
例3:外界センサー
人間モデリング・最少パラメータ
 Standard parameters = full set of inertial parameters
 Base parameters = identifiable parameters from
inverse dynamics only (YB of full rank)
15
同定のため適当な動作は?
 リグレサーの状件数
Cross Validation
例3:外界センサー
人間・RT同定
 モーションキャプチャ
(motion analysis)
 床半力 (Kistler)
 Screen for visual
feedback
 PC for motion
acquisition
 PC for real-time
computation
例3:外界センサー
人間・RT同定
From motion capture (RT IK computations)
and force-plates (RT generalized effort computations)
base and SP are identified with RT method
(recursive algorithm)
Geometric scaling and initial SP
Measure the geometric parameters of the
model from motion capture: automatic
scaling from marker positions
Estimate the initial SP f ref from geometric
parameters and database of human body.
Initial geometric parameters and
SP identification for 3 candidates
with 3 different morphologies:
1.73m 58Kg, 1.62m 54Kg and
1.76m 76.3Kg
Persistent Exciting trajectories
Using RT color changes to specify the links
not yet completely identified, results are
obtained in a shorter time with more
accuracy.
3. Real time identification of the human dynamics
Persistent Exciting trajectories
同定結果
Comparison of some identified parameters
with literature [Young et al. 1983]
応用:スポーツトレーニング
 33 year old female
 Stanton marathon training program in preparation
for the 2009 Tokyo Marathon.
 Training program = 16 week program
 5-days a week running schedule
 Gradually increased running distance from 20km/week to
80km/week in the peak 13th week, then tapering in the
final 3 weeks before the race.
 Prior to that subject runs 25km/week
 Record on a weekly basis / several sessions omitted
応用:スポーツトレーニング
例4:関節粘弾性の同定
 Biomechanics developed methodologies for
measuring various dynamics properties of the
human body
Determination of standard values of joints’ visco-elastic
properties based on well calibrated measuring equipments
and averaging of data of many subjects.
 Equipment need mechanical stiffness and accuracy
⇒ heaviness and bulkiness (dynamometer)
⇒ not applicable to everyone, specially to people under
rehabilitation and medical treatments.

⇒ System to measure patient-specific visco-elastic
properties of limb joints without pain and constraints
highly required.
例4:関節粘弾性の同定
Joint modelling
Model of visco-elastic
properties adapted
from biomechanics
most popular model
+Identification of human joint passive dynamics, Proc. of the IEEE Int. Conf. on Robotics and Automation, pp
2960-2965, 2006.
+Identification of Human Limb Visco-Elasticity Using Robotics Methods to Support the Diagnosis of Neuromuscular
Diseases, Int. J. of Robotic Research (to be publiched)
神経病の診断
実験結果
Possible discrimination of patients
21
終わり
 参考論文


W. Khalil and E. Dombre. Modeling, identification and control of robots. Hermès Penton, London-U.K, 2002.
Y. Nakamura, Advanced Robotics: Redundancy and Optimization, Addison-Wesley Longman Publishing Co., Inc,
1990.

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baselink equation of motion,” Proc. of the Conf. on Robotics and Mechatronics, 2P1-F09, 2008, (in Japanese).
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