Human Movement - Science topic

Thanks to everybody who recommended or followed the post to make it more visible!

Hello Dennis Shavelson

Some journals will permit this and some will not. If there is any doubt with your selected journal, I suggest you may need to contact them directly. I would hate for them to say you have '"breached the rules" before you submitted it. I say this because sometimes I may have published the abstract from having presented at a conference and some journals say that this rendered my submission as "null and void'". I would just take it to another journal where it IS ok.

Hello Navindra,

If your only goal with this simulation is to find the eigen modes/frequencies and damping, a gaussian white noise bandpass filtered to the range of your suspected natural frequencies of interest (e.g. number of floors/10 estimates f0 for buildings) will do the job without fail. The magnitude of the white noise must be sufficient to excite your structure. (usually ~0.2g suffices in my experience for buildings) I suggest using Automated FDD for OMA to ease your calculations.

I hope this helps! Good luck with your research :)

Hello! Thank you so much for your response and suggestion.

My recent experience with mixing GIS data and Space Syntax analysis is:

1- The lines representing streets in GIS database are too complicated. They are not 'axial lines' that are fewest and longest. This character fails to meet Hiller's concept and definition of a fewest axial line map, which is used to represent a city's street network. The axial line map used for space syntax analysis should be built properly following the 'fewest' and 'longest' principles.

2- I have to: (a) export GIS-origin street map into AutoCAD, (b) revise the map into a proper axial map, keep the original coordinate and projection; (c) run space syntax analysis with DepthMapX; (d) export the outcome map to QGIS, (e) open in QGIS and use this map rather than original GIS-origin street map.

Problem 1: The street lines are now much simplified and somehow 'inaccurate'. But it is acceptable for city-scale analysis.

Problem 2: Time consuming, especially step 2(b).

Could anyone advise a faster way?

I know there is a Space Syntax plugin for QGIS, but it isn't available for the most recent long-term release version of QGIS (3.10).

I try exporting QGIS street map of a city into DepthMap, it takes forever for DepthMap to calculate and analyse (I am using both apple Mac M1 and Intel i5 2020 computers).

Thank you in advance for participating in this discussion and suggest possible solutions.

Best wishes

Ha

Hello! Try to use Kinovea (free soft). Nice program for automatically tracking.

In my view, due to the COVID-19, we are realizing the full potential of the web-based physical Activity and exercising at home. It appears that the pandemic has demonstrated that the internet can expand the scope of educational and professional care and include people who possibly were not comfortable in a traditional physical activity setting. People will cherish more PA, including RT, even in non traditional setting.

According to its quality and gender

here is an article i read recently though my interest in dance, the dancer/performance and subject matter in the artilce-outside academia. i will be glad to help you perform a literature review as i am sure there are many resources out there.

Haven't seen the book yet - just ordered a used copy.

HI Niluka,

As far as I know several professional softwares (like premier) have tracking features. Otherwise, a very good solution is kinovea. Kinovea is a video player for sport analysis. It provides a set of tools to capture, slow down, study, compare, annotate and measure technical performances. And, most importantly, is free and open source.

bye bye

Dear @Carolina Vila-Chã,

I am so grateful for your useful information.

Best wishes.

Dave.

I think there are many ways in which one could respond to you question, Jorge Garza-Ulloa , one viewpoint I would consider is related to the incumbent equations/models that may be used to theorize gait. this paper by Kuo and Donelan gives some nice information throughout the article, and demonstrates the idea of the inverted pendulum (and similar) models of human gait. I'm not sure how far that goes to answer your question, but hopefully helps in some small way. Best wishes, Cain

because force plates in pathway are limited. the second heel contact was not on the force plate ...

I think it all comes down to matching your outcome measure to your research question. For instance, if you want to look at variability of a dataset, perhaps the standard deviation of the COP or even a nonlinear approach such as approximate entropy to look at the randomness within a sample.

Another example is using fractal dimension, which looks at the 'predictability' of a given path, where one would assume a more complex path represents either an altered postural control strategy, or perhaps dysfunction of the sensorimotor system.

I suggest reading this article by Prieto et al, 1996, regarding various COP outcomes, and it includes the calculations for each: http://www.ncbi.nlm.nih.gov/pubmed/9214811

Within my lab, we use time-to-boundary, which is a derived signal that looks at the direction and velocity of the COP signal with respect to the borders of the foot. See Hertel et al, 2006: http://www.ncbi.nlm.nih.gov/pubmed/16760569 There are other outcomes like this, and this would represent the amount of time it would take for an individual's COP to pass outside the borders of their foot (base of support), thus the assumption is made they would lose balance or be in a compromised position.

The 95% confidence ellipse is also a popular outcome, representing the locations of the COP, or another measure of COP area, if you want to look at the area of COP displacement.

Ultimately, my first point will help you best. Figure out which outcome will tell you most about your population or your balance task, and then use it. 

I hope this helps,

Hi Bas

Thanks so much

Dear Javier,

I've found some papers which deal directly with this subject.

I hope this helps.

my dear Harshvardhan Singh 

Thank you very much for your interest I have benefited a lot from the publication that sent

Accept my greetings

Dear Javier,

The different variables comprised traditional linear and non- linear methods as proposed by diverse authors [....]. Duarte & Freitas (2010) subdivided the parameterization methods roughly into two groups: First, the traditional parameters which refer to estimations of the overall size of COP excursions, second, structural posturographic and more sophisticated parameters which describe temporal pattern of the time series in a nonlinear manner [...]. The former ones are used as indicators of postural stability outlining COP displacements as random fluctuations. According to this theory, larger COP deflections are associated with less stable balance and in a next step with aging and disease. Consequently, the assumed random properties are treated as unwanted noise and averaged out. Temporal structures of the displacements are disregarded. In contrast, nonlinear methods determine the time de pendent structure of the time series. Against this back- ground, both analysis techniques create complementary information [...]. In my vision, these two parameters (average and SD of COP) are complementary, they are classified in the first group (traditional paraemters), however I prefer to use the SD of COP because the results are most complet. You will can to show the results of these two variables, but depending of the case it will be unnecessary. 

Look some papers which I sent to you. I believe they can clarify your ideas.

Best regards.

Dear  Sumeet Gupta, first of all the max. principle strain is the positive, i.e. max. tensile strain value that is calculated (the min. principle strain is at the other end of the scale which could be a negative strain or compressive strain). The max. von Mises stress is an equivalent Parameter, which is always positive as it comprises the diagonal stress Tensor componends Sigma-xx, Sigma-yy and Sigma-zz in quadratic form. Hence the max. von Mises could be either mostly tensile or compressive or even a combination of both. Since you have the tensile strain on top (probably Epsilon-xx), the max. v.Mises value at the bottom should result mainly from compressive stresses (Sigma-xx) due to bending around y axis, but with possible contributions from either tensile or compressive values of the other stress components (Sigma-zz). You can check the dominating stress components directly by ploting the single stress components. As main FEM quantity stresses and strains are always available / calculated, you do not Need to recalculate anything. Just post analyze your Output datafile.

Dear Marcelo,

try also to check this paper, page 49. You'll find a comprehensive description of balance/movement priorities.

Best

Matteo

Hi Esmaeel,

First, could you explain why you want to know the instant of ball release? Are you trying to compare with previous research? Or are you trying to answer a specific research question? How accurate do you have to be?

Second, could you elaborate on what data you have measured?

Frankly, I don't think you can reliably detect the instant of ball release if you only have joint angle data. Sure, you can estimate it happens at for example the the peak velocity of the wrist angle, but you will never be entirely accurate due to (functional) variability. Ultimately, you would have to validate your assumption that instant of release and some variable of joint angles are related.

Good luck,

Rens

Hello. I imagine that it depends what you mean by abnormal event. From my point of view, this type of problem takes two forms. The first is an image processing problem where you look in a single frame for an object with a certain feature. This can be reduced to a parameter estimation problem. The second way is to view the problem as one of change detection, i.e., the abnormal event corresponds to a change in the dynamics of the underlying system (think of you car engine when something inside it breaks). There is a large literature on change detection, which can be approached in many ways. My experience of this type of problem is from the area of manoeuvring target tracking. Hopefully you can find some useful references for your work as quite a lot has been done in this area (see link), although the sensor is usually a radar system rather than a video sequence.

I think that Fourier's transformation doesn't correspond to the task solved by You. It assumes strict frequency of the studied process. This is uncorrect. Use of a veyvlet transform would be more correct. From the point of view of presentation it is better to use Morle wavelet calculated as integral with linear frequency scale. In this case unlike Dobesh's veyvlet the narrow frequency peak is shown well, but there are hidden problems with its temporary localization.  Problems of masking of frequencies and Nyquist's frequency won't disappear anywhere in principle. We so investigated temporal change of frequency of a physiological tremor. This process significantly more slowly, than what you try to investigate The truth our employee wrote the program.

Hello Jenn - You can search for keywords like "entrainment" or "groove".

You will find references to a large body of research in the following (very recent: November 2014) paper:

Burger, B., Thompson, M. R., Luck, G., Saarikallio, S. H., & Toiviainen, P. (2014). Hunting for the beat in the body: on period and phase locking in music-induced movement. Frontiers in Human Neuroscience, 8. doi:10.3389/fnhum.2014.00903

It is downloadable here: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4224089/

Best, Olivier

There is a new project (The Voice of the Body) from Samsung that plans to give people access to a large database of recorded movements. You can find more details on their website: http://www.voiceofthebody.io/bioinformatics

It's not available yet, but I think that may be a good option for what you want.

The best system I know to analyze movements is OpenSim, an open project of the Stanford University

Vladimir Zatsiorsky is completely right in stating that in eccentric contractions dF/dt can be very fast. Mind that a muscle is composed of a contractile and an elastic element in series. For fast eccentric actions dF/dt = dF/dx . dx/dt = k.dx/dt in which k is the elastic stiffness, thus dF/dt mainly depends on the lengthening speed dx/dt, not on the muscle contractile properties.

For comparing muscle contractile properties, you may better use isometric actions. For this case you can find some formulas in my paper 'Muscle mechanics and neuromuscular control, J. Biomechanics 36:1031-1038 (2003) in Appendix 1.

Since you have a discrete task (if you consider the success of shooting as goal and the release position, orientation and velocity as body level states) the GEM will be the best approach. I agree with Klaus Blischke that the CR (correlation by randomization) approach introduced by Muller also will be a good choice. but the sensitivity analysis an generality of GEM makes it the best choice. maybe it seems a little complex but really it is not that complex. I re command you to take a look at recent paper of Joseph Cusumano and Jonathan Dingwell. " Movement variability near goal equivalent manifolds: Fluctuation, control, and model-based analysis". specially section 4.4, general experimental hypotheses. they are really interesting. although some of them are not testable for your task (like DFA analysis that needs quite large number of trials) you can give some help from engineering groups working on the subject in Iran. 

the UCM analysis will be easier to run. It is appropriate for analyzing the continues task of throwing too (in a identical initial position until the ball release moment). 

the more easier approach will be PCA which you can run in MATLAB with a single function like princomp.

Generally it depends on your hypotheses and  level of analysis and interpretation you want to have. 

This seems relevant to what I am trying to ask. Thank you!

Thanks Matteo, but what I am looking for are references about characterisation of body shapes in motion, whereas the problem addressed in papers like the one you cite is rather the opposite: how to cancel the motion of such soft structures to estimate the rigid motion of the underlying bones. Of course, one of the strategies for that is trying to model the motion of the muscles and skin, but that is not the focus.

Hello,

I hope you will not mind my contribution here as it does not shed any light on your question in any way but might help someone who may read these Q&A in future.

The best approach in any experiment is to plan it ahead and decide what you will measure, how and what will you do with the measurements. If any experimental work is being undertaken on human subjects be they normal or patients the law in UK and I think that goes for all of EU, requires you to secure a favorable opinion from a research ethics committee (REC). Such a committee would not grant permission for experiments unless there was a clear and valid protocol for the study, which goes back to what I wrote at the start, i.e. plan the measurements and the analysis etc.

Best of luck

This is a very interesting topic. I suggest reading of:

Describing patterns of physical activity in adolescents using global positioning systems and accelerometry.

Maddison R, Jiang Y, Vander Hoorn S, Exeter D, Mhurchu CN, Dorey E.

Pediatr Exerc Sci. 2010 Aug;22(3):392-407.

Yet, every motion can be defined by a rotation about the helical axis and a translation along the helical axis. To avoid the oversimplification, all you need to do is to release the constraint that the helical axis is fixed in space throughout the movement. Kane's Advanced Spacecraft Dynamics book does an excellent job in defining the helical axis.

I have not found a pattern in the turn around time. Although most articles are avaialble electronically, the audience you are trying to reach should be taken into account. Some journals very much target practioners, who may not be searching for articles electronically, but will peruse specific journals

In cases where we were not sure about the durability of the 'stickies' or double-adhesive tapes that are suggested for the ActviPal we decided to use OpSite Flexigrid surgical tape/film that is really thin and flexible and can even be used to seal the device against water (e.g. if you want to leave it on for taking a shower).

The 'belt-around-the-leg' approach did not really work well for us because of the conical shape of the thigh: The device either does not stay in place but slips down or you have to make the belt/strap so tight that it is uncomfortable or even reduce blood circulatiopn (both not really practical for long-term recording).

Thanks a million, Jonas Rubenson and Páblius Silva. Rrgards