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Computation of ground reaction force using Zero Moment Point
KTH, School of Engineering Sciences (SCI), Mechanics, Biomechanics. (KTH BioMEx Center)ORCID iD: 0000-0001-8699-7013
KTH, School of Engineering Sciences (SCI), Mechanics, Biomechanics. Karolinska Institutet, Stockholm, Sweden. (KTH BioMEx Center)ORCID iD: 0000-0001-5417-5939
2015 (English)In: Journal of Biomechanics, ISSN 0021-9290, E-ISSN 1873-2380, Vol. 48, no 14, 3776-3781 p.Article in journal (Refereed) PublishedText
Abstract [en]

Motion analysis is a common clinical assessment and research tool that uses a camera system or motion sensors and force plates to collect kinematic and kinetic information of a subject performing an activity of interest. The use of force plates can be challenging and sometimes even impossible. Over the past decade, several computational methods have been developed that aim to preclude the use of force plates. Useful in particular for predictive simulations, where a new motion or change in control strategy inherently means different external contact loads. These methods, however, often depend on prior knowledge of common observed ground reaction force (GRF) patterns, are computationally expensive, or difficult to implement. In this study, we evaluated the use of the Zero Moment Point as a computationally inexpensive tool to obtain the GRFs for normal human gait. The method was applied on ten healthy subjects walking in a motion analysis laboratory and predicted GRFs are evaluated against the simultaneously measured force plate data. Apart from the antero-posterior forces, GRFs are well-predicted and errors fall within the error ranges from other published methods. Joint extension moments were underestimated at the ankle and hip but overestimated at the knee, attributable to the observed discrepancy in the predicted application points of the GRFs. The computationally inexpensive method evaluated in this study can reasonably well predict the GRFs for normal human gait without using prior knowledge of common gait kinetics.

Place, publisher, year, edition, pages
Elsevier, 2015. Vol. 48, no 14, 3776-3781 p.
Keyword [en]
Prediction, Inverse dynamics, Forward dynamics, Gait
National Category
Biomedical Laboratory Science/Technology Biophysics
Identifiers
URN: urn:nbn:se:kth:diva-180136DOI: 10.1016/j.jbiomech.2015.08.027ISI: 000366064000006PubMedID: 26482731ScopusID: 2-s2.0-84947093227OAI: oai:DiVA.org:kth-180136DiVA: diva2:893863
Note

QC 20160113

Available from: 2016-01-13 Created: 2016-01-07 Last updated: 2016-05-24Bibliographically approved
In thesis
1. Constrained Optimization for Prediction of Posture
Open this publication in new window or tab >>Constrained Optimization for Prediction of Posture
2016 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The ability to stand still in one place is important in a variety of activities of daily living. For persons with motion disorders, orthopaedic treatment, which changes geometric or biomechanical properties, can improve the individual'sposture and walking ability. Decisions on such treatment require insight in how posture and walking ability are aected, however, despite expectations based on experience, it is never a-priori known how a patient will react to a treatment. As this is very challenging to observe by the naked eye, engineering tools are increasingly employed to support clinical diagnostics and treatment planning. The development of predictive simulations allows for the evaluation of the eect of changed biomechanical parameters on the human biological system behavior and could become a valuable tool in future clinical decision making. In the first paper, we evaluated the use of the Zero Moment Point as a computationally inexpensive tool to obtain the ground reaction forces (GRFs) for normal human gait. The method was applied on ten healthy subjects walking in a motion analysis laboratory and predicted GRFs are evaluated against the simultaneously measured force plate data. Apart from the antero-posterior forces, GRFs are well-predicted and errors fall within the error ranges from other published methods. The computationally inexpensive method evaluated in this study can reasonably well predict the GRFs for normal human gait without using prior knowledge of common gait kinetics. The second manuscript addresses the complications in the creation and analysis of a posture prediction framework. The fmincon optimization function in MATLAB was used in conjunction with a musculoskeletal model in OpenSim. One clear local minimum was found in the form of a symmetric standing posture but perturbation analyses revealed the presence of many other postural congurations, each representing its own unique local minimum in the feasible parameter space. For human postural stance, this can translate to there being many different ways of standing without actually noticing a difference in the efforts required for these poses.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. 37 p.
Series
TRITA-MEK, ISSN 0348-467X ; 2016-11
Keyword
Static optimization, Multibody system, Musculoskeletal model
National Category
Applied Mechanics
Research subject
Engineering Mechanics
Identifiers
urn:nbn:se:kth:diva-187488 (URN)978-91-7729-036-0 (ISBN)
Presentation
2016-06-15, E31, Lindstedtsvägen 3, Stockholm, 10:00 (English)
Opponent
Supervisors
Funder
Swedish Research Council, 2010-9401-79187-68
Note

This work was financially supported by the Swedish Scientic Council(Vetenskapsrådet) grant no. 2010-9401-79187-68, the ProMobilia handicapfoundation (ref. 13093), Sunnerdahls Handicap foundation (ansökan nr 11/14),and Norrbacka-Eugenia foundation (ansökan nr 218/15).

Available from: 2016-05-24 Created: 2016-05-24 Last updated: 2016-05-24Bibliographically approved

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