Biomechanical Analysis - Science topic

Biomechanical indicators play a critical role in enhancing the effectiveness of training interventions aimed at improving front handspring performance on the vault. Key indicators—such as run-up velocity, angle of take-off, force production at the springboard, contact time with the vault table, and body alignment during flight and landing—directly influence performance outcomes.

Modern training approaches utilize motion analysis, wearable sensors, and 3D kinematic tracking to quantify these indicators in real-time. This data-driven feedback allows for highly individualized exercise programming, targeting specific mechanical inefficiencies.

Furthermore, integrating contemporary educational strategies—such as personalized learning, immediate video feedback, and AI-assisted motion correction—has shown significant promise in accelerating motor learning and technical precision in gymnastic vaulting.

By aligning biomechanical analysis with pedagogical innovation, coaches can design more effective, evidence-based drills that lead to measurable improvements in the front handspring vault performance.

This is a great question—thanks for sharing your project. You're tapping into a complex but fascinating area of biomechanics, especially when using wearable sensors like tri-axial accelerometers to assess dynamic athletic movements like pitching.Short Answer: Yes, using synthetic (vector magnitude) acceleration from a 3-axis accelerometer as a kinematic or kinetic index has limitations—mainly because it lacks directional context and can include non-biological artifacts (like gravity or sensor noise). It's useful for overall intensity but problematic for deeper biomechanical interpretation without careful processing and validation.Let’s break down the key issues and considerations: 1. Loss of Directional Information When you combine the three axes into a synthetic (or resultant) acceleration (i.e., √(Ax² + Ay² + Az²)), you lose the vector direction of the motion. For throwing, the direction of acceleration matters (e.g., anterior-posterior vs medial-lateral), especially when you're trying to interpret it in terms of joint motion, torque generation, or sequence of motion in the kinetic chain.2. Influence of Gravity Accelerometers measure total acceleration, including gravitational acceleration (1g downward). Unless you apply a method to separate dynamic and static components (e.g., using orientation from IMU data like gyroscopes), your synthetic acceleration will be contaminated by gravity—especially problematic in dynamic motions like pitching.3. Soft Tissue Artifact The accelerometer is placed on the skin or clothing, not directly on the bone. Rapid movement (like pitching) causes the sensor to move independently of the bone due to skin deformation, muscle oscillation, or impact forces, especially during high-speed events like ball release.4. Sensor Drift and Noise Without sensor fusion (i.e., combining accelerometer with gyroscope and magnetometer), you might get drift or spike artifacts during rapid accelerations. High-frequency noise or sampling issues can lead to misinterpretation of peak values or timing.Recommendations for Your Study Use Synthetic Acceleration for: General intensity measures of movement. Comparing inter-subject or intra-subject variability (e.g., during different pitch types or effort levels). As a trigger or threshold detector (e.g., to find max effort phases or ball release events).

Be Cautious If You Want to: Infer joint kinematics or segment torque from synthetic acceleration alone. Make assumptions about the sequencing of the kinetic chain without looking at relative segment orientations and velocities. Use the data as a standalone biomechanical marker for shoulder or elbow stress.A Better Approach? If you're using Noraxon's MyoMotion, you're already getting orientation/quaternion/rotation matrix data from the IMUs—not just acceleration. That’s a huge plus!So instead of just using synthetic acceleration: Look at angular velocity of the upper arm and trunk. Analyze segment orientations and timing relationships between pelvis, trunk, and arm. Consider computing relative joint angles and angular accelerations using MyoMotion’s joint models.Final Thoughts: Your idea of using acceleration as a kinetic chain indicator is valid in spirit, but needs context and additional data interpretation. Use synthetic acceleration as part of a multivariate analysis,

Calculating 3D Lagrange equations in gait analysis is a sophisticated process that involves biomechanics, physics, and mathematical modeling. Here’s a step-by-step breakdown to guide you through how it's typically approached in the context of kinematics data for gait analysis:

Step 1: Understand the Physical System

In gait analysis, the human body is often modeled as a multi-link rigid body system, with joints acting as rotational or translational constraints.

Step 2: Define Generalized Coordinates

Choose a set of independent variables that fully describe the system.

Step 3: Compute Kinetic and Potential Energy

Kinetic Energy (T):

Where:

You’ll extract velocities and angular velocities from your motion capture data (or compute via numerical differentiation of position data).

Potential Energy (V):

V=mghV = m g hV=mgh

Step 4: Apply Lagrange’s Equations

Step 5: Include External Forces (Generalized Forces)

You need to include:

Convert these forces into generalized coordinates using virtual work principles or Jacobians.

Step 6: Solve or Simulate the Equations

Depending on the goal:

Tools You Might Use

good luck

3. Chest Compression (C)

5. Rib Deflection / Deflection Rate

Regulatory & Dummy-Specific Criteria

Lees, A., Vanrenterghem, J., & De Clercq, D. (2004). “Understanding how elite athletes jump: A biomechanical analysis of the standing long jump.” Bobbert, M.F., & Van Soest, A.J. (2001). “Why do people jump the way they do?” Kubo, K., Kanehisa, H., & Fukunaga, T. (1999). “Influence of elastic properties of tendon structures on jump performance in humans.”

First, represent the Zernike polynomial as a complex polynomial in polar coordinates (r, θ) using the Zernike radial polynomials Rl(p) and angular harmonics Pm(θ). Then, evaluate the polynomial at a grid of points on a circular domain (e.g., using a radial and angular resolution). Finally, use the complex values to create a 2D array representing the surface height at each point. You can use libraries like Python's NumPy and SciPy to perform these steps. For example, you can use the `numpy.meshgrid` function to create a grid of (r, θ) values, and then evaluate the Zernike polynomial using NumPy's `polyval` function.

Follow these steps:

1. Import the Geometry: Load the pelvis and sacrum bone geometry file into a 3D modeling software or a CAD program capable of handling complex geometries. Ensure that the file format is compatible with the software you are using.

2. Duplicate the Bone Geometry: Create a duplicate copy of the original bone geometry to work on. This will allow you to preserve the original bone geometry while creating the cortical bone shell.

3. Scale the Duplicate Geometry: Scale up the duplicate bone geometry uniformly by 4mm in all directions. This will create a larger version of the bone geometry, which will serve as the outer boundary for the cortical bone shell.

4. Offset the Duplicate Geometry: In the CAD software, use the "offset" or "shell" feature to create a new surface that is 2mm away from the outer surface of the scaled duplicate bone geometry. This will generate the cortical bone shell with the desired thickness.

5. Boolean Operation: Perform a Boolean subtraction operation between the original bone geometry and the cortical bone shell geometry. This will remove the original bone geometry from the cortical bone shell, leaving behind the shell itself.

6. Clean and Refine the Geometry: After the Boolean operation, you may need to clean and refine the resulting geometry. Check for any overlapping or intersecting surfaces and make necessary adjustments to ensure a watertight and smooth cortical bone shell.

7. Create Holes for Screw Insertion: Identify the locations where you want to insert screws and create holes in the cortical bone shell geometry accordingly. The size and shape of the holes will depend on the specifications of the screws you intend to use.

8. Export the Final Geometry: Once you have completed the cortical bone shell and added the necessary holes, export the final geometry in a suitable file format (such as STL) that can be imported into a finite element analysis (FEA) software.

I am NOT a doctor, it should be used only for models.

Hope it helps: partial credit AI

I think you can try something like this

Hi Alexander,

I don't know the answer. When I clicked on questions this was one of the top ones and it took me back to my undergraduate days and I thought it was intriguing.

I completed a project and published a paper on the development of a Glenohumeral Test rig back then and I also know of other researchers who were working on similar bio-mechanical projects. I've attached the paper below and a few others. Maybe you could use them for reading, have a look at the referencing, it might provide you with useful papers related to your project. It might not, but I just thought it could be of some use to you.

Hope they might be of some use.

Best Regards

Martin

weight acting capacity, stiffness, material, strength etc.

make more than one surface

Dear Karthigeyan,

Many thanks for the advice. In fact my problem was that the forces were not in equilibrium in y static model, which caused convergence problems. As a I only needed my geometry to move in the direction of my force, I was able to solve the problem by using linear elements C3D4 instead of Quadratic. Thus, rotation was not allowed by using these elements without compromising the result.

Best!

Hi Alireza,

First, what is the type of motion capture system do you have? Most of them actually have their own synchronization box, which serve as trigger in or trigger out for other external device including IMU. However, if your IMU sensor is a standalone sensor without any base to connected, then you can try an option to synchronize it by using some movement that cause spike on IMU signal like jumping.

Hope this helps you

I am not familiar with that exact system, but if you have the option of recording ECG you could generate a timing signal and attach that to both the ECG and EMG sensors to use as a synchronisation signal. Just ensure you generate an appropriate voltage and current so as not to damage the equipment.

You might be able to couple the rhodamine carboxyl to an alginate hydroxyl with a water soluble diimide coupling reagent.

Or use a different dye....

Great, thank you. I'll send you a message.

It seems you are trying to solve a classic problem of human activity recognition based on body-worn sensors. A very good starting point for different datasets, mainly based on acceleration only, is this paper:

I wouldnt recommend using a naked CNN for testing. A classic machine learning approach with a proper preprocessing, some good extracted features (e.g. simpe statistical features, PCA or Codebook) and a classification that supports the understanding of the problem (e.g. kNN or SVM). Good luck and have fun trying :).

Dear Mehdi,

I heard, for example, the power of ABAQUS in mesh generation is better than ADINA. A researcher told me that he had a 3D model and he couldn’t prepare a meshed model of it using ADINA and he had many errors and problems, but he prepares the meshed model of that 3D model very easy using ABAQUS. Do you agree with him?

The cheapest is likely to be either a 2D video camera and free software.

The trouble you will find is placing a camera in the correct location. Using a treadmill is unlikely to be a good move with this patient group, therefore you will need multiple cameras to catch more than a step or two initiation of gait.

You could try an app on a mobile phone, you can either find one that tracks movement or one that streams data via Bluetooth. Most phones have both at least one triaxial accelerometer and gyroscope included, so depending on your skills with signal processing this could be a reasonably cheap approach.

Chee Loong Chin Thanks a lot for your helpful answer.

Units were not consistent. The mimics soft had defined density in g/cm3 but because distances was in mm; the consistent unit for density was tonne/mm3.

Problem solved and thank you again.

You can look at this link https://scholar.google.com.ua/citations?hl=ru&user=rzrn01UAAAAJ&view_op=list_works&sortby=pubdate

please see

How have you defined the contacts?

Franco

I Think that your fatigue implants specimens diameters must modeling according the more risky case which is the smallest diameter

I think that it is very interesting thought, and apparently I remember seeing that kind of scene from a movie (I cannot remember the title of movie...).

What process or mechanism would be taken?

Both kinematic and kinetic.

We want to identify people by their gait cycle video. Do people have a unique gait cycle?

I believe that people have a unique gait pattern. However, you should consider that each person have somewhat variability within their gait cycle. In other words, there are slight variations (wider/narrower/shorter/longer steps) within a person during step by step movement.

In my opinion, it is impossible but not feasible/efficient due to following reasons.

First, the variation (within person)/changes of gait cycle might be bigger than the fingerprints so that it may require constant update to an identification system. For example, a person's gait cycle can be changed due to different footwear, results of an injury, and aging. On the other hand, fingerprints are relatively stable to change (of course it changes/fades as time goes by...).

Second, fingerprints provide a reliable means of personal identification. To achieve the same level of reliability, thousands and thousands of people's gait cycle should be tested and analyzed to see the differences and similarity. Maybe at the end, you may conclude that the variation of gait cycle is not unique as much as the fingerprints (between persons).

In conclusion, I think that the combination of both can be beneficial for some closed/secured setting. To my knowledge, however, the gait cycle won't be able to completely replace the fingerprints identification.

Hope my answers are helpful to advance your thought.

Tools required for Gait-Kinematics only:

For kinematics, these will suffice. If you also need Inverse Dynamics (joint force, torques and power), then you will additionally need 6 component force platforms (1 or more) to record GRF and COP, and of course Inverse dynamics capability in the software.

Hello Mohammad.

In the past I have done some electrospinning on aluminium foil. To collect some samples I put thin glass coverslips on top which allowed me the collection of the sample (with the aid of a scalpel).

Also, you can try with some teflon tape on top of the aluminium foil, which should detach easily.

Hope it helps,

Hector

Dear Saman ,

- There are several cantilever suppliers in EU and US as mentioned in the other answers.

- For cantilever based research, different groups normally using the simulation (FEM, ANSYS) to simulate and come up with their own cantilever designs. The variations are materials ( often are Si, low stress SiN, polysilicon, some polymers..), dimension, and even new cantilever formats (single, arrays, added materials ( often Au) for facilating the biding of receptors, new structuring...). In this way, they bring the added values in research and also make easy of later publishing the paper. This called custom-made designs.

- At Nanosens, we have been offering custom-made cantilevers for various customers (www.nanosens.nl). Most of the case we provide ultrathin ( down to 20 nm in thickness) low-stress SiN cantilvers. We are not often offer Si cantilevers as to make it we need to start from SOI wafers, and currently comercial available SOI , especially SOI with the device thickness in the micron range, has poor device thickness uniformity ( ca. 25% for 4 inc. wafer), subsequently lead to large variation in final cantilever thickness. Low-stress LPCVD SiN layer can be realized within 2 % of thickness variation.

Is there a final form of this device or technology?

No. As mentioned above, there are so much variations. Also, the cantilever forms depended on the measurement setup ( singles or arrays, range of frequency, static or dynamic modes, and recently coupling of opto + mechanics for optomechanical cantilevers...)

Has this device ever been used in a clinic or hospital complex?

Not yet. But I personally think it comes soon . You can take a look at the research group of Prof. Javier Tamayo and Montserrat Calleja , BioNanoMechnics Lab in Spain http://www.imm-cnm.csic.es/bionano/en

You may see that they have been making a great progress to push this device to practical applications.

- Finally, measurement setup is also a crucial important factor for the cantilever research. The Scala system from MECWINS is a personally recommended as its cost is acceptable, and simple in both operation and maitenance.

I recommend using the CVMob free system available at https://sites.google.com/site/cvmobufba/ that has been validated for gait evaluation (http://dx.doi.org/10.17267/2238-2704rpf.v7i4.1648 ) and for postural evaluation (http://dx.doi.org/10.1097/JCE.0000000000000226).

The horizontal system in terms of power sharing is equal. Two joined company are able to maximize the resources on an equal level. However, Vertical system is mostly controlled by one party on the top.

If you're interested which system works based on your organization, and learn more cases in the marketing system. You can check out Career Academy. We use them for our office managers' training Project Management (Project Management, Business Skills, IT & CyberSec Online Certification Training)

I agree with Dr.Diachkova. Knee varus deformity predisposes to degenerative changes medially and progression.

I will suggest for power spectrum analysis on the data

CatWalk XT - a gait analysis system for rats and mice. 

Hi Saul,

In this article we addressed your question experimentally,

Hope it helps !

Ciao

Alessandro

Interesting but no experience

fantastic - Thank you so much!!!

Thank you professor brain for all your expertise  informations its very interesting

Thank you sofia for your answer and attachment paper .

You can use navicular drop or normalized truncated navicular height also according  to  your sample population

Imran, 

Oftentimes, mixed methods are employed.  For example, in a well-known study involving  R. Roy and V. Edgerton (1992), they estimated muscle volume from living subjects using serial axial view MRI scans.  However, the PCSA equation variables for muscle fiber length (i.e., muscle bundle length) and pennation angle were derived from their earlier cadaveric work from the seminal 1983 publication with Wickiewicz.  

Of course, there are other ways of accomplishing this task. More contemporary studies feature investigators that use imaging methods such as sonography to obtain in vivo estimates of muscle bundle length and pennation angle.  The brief list of links below highlight some of the classic methods and more recent techniques, along with some of the limitations associated with the imaging methods and PCSA estimates in general.

The study of muscle fatigue with EMG can be done with the median frequency obtained from the power spectrum. However the EMG signal needs to be very stable and this only happens in an isometric contraction. In other words, the calculation of the median frequency of the EMG signal can not be done based on a dynamic muscle contraction. You can have a dynamic exercise which induces fatigue and two isometric contractions, for instance, before and after. In these two isometric contractions you can calculate the median frequency.

I agree wih the first answer, but you can only find the knee angle. Is it sufficient?

Good luck. 

From my empirical knowledge all data should be taken into consideration. This type of study, that implies the use of an insole, could offer information over a larger range of anthologies, because it can measure in close and open chain.

Only if the data is beyond normal limits it should be used a threshold. In this direction there is a lot to talk about because I haven't heard of such normal limits, they depend on the situation of analysis environment. All that we know is that friction is given by friction coefficient multiplied by N and the ground reaction force is mass multiplied by acceleration plus any external or internal forces that might appear. It would be interesting to find a relation between this parameters that could offer a coefficient to calculate the limits of the forces considering age, mass, and different anthropometrical values.

In conclusion, I would use those values obtained from the opened swing phase of locomotion.

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,

I am not sure you will find easily this information on text books, however, I am sure you can find few PhD thesis using hyperelastic models for soft biological tissues. You can nevertheless find easily hyperelastic theory for polymers (which is the same theory)

Some key aspects of this are :

- Usual strain energy derives from classical thermoelasticity for hyperelastic materials.

- limitations are based on the fact that hyperelastic behavior depends on identification parameters on simple test such as simple tension or biaxial tension not valid on complex load scenarios

Application to soft tissue depends on two factors : (i) hypothesis of homogeneity of the structure, (ii) validity of tissue microstructureal behavior to the model you use.

Regards,

What VI´s did you download? Why didn´t worked? If it was because of the newer version you can ask for a version conversion in the forums of NI (first link). All the compatibles toolkits available for that version are listed in the second link. I´m sure you can process biomedical data on labview 8.5, the question is, how are you gonna acquire the data. Also, you can download the evaluation softtware of labview (newer version) directly from NI. Best regards.

The Nyquist sampling theorem dictates your sampling frequency. You first must determine the highest frequency intrinsic to the process you are trying to record. The Nyquist theorem states that your sampling frequency must be at least twice that of the highest intrinsic frequency. If you don't, aliasing will occur. Often, the ideal sampling frequency may be more like ten times that of the highest intrinsic frequency. 

Dear Javier,

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

I hope this helps.

Unfortunately no, I could not find the Humos 2 hbm model on a public source. To be honest I did not contact ESI either. 

Paul W. Brand, FRCS, and the bioengineers with whom he worked at the US Public Hearth Service, Carville, LA (USA) did considerable research regarding soft tissue response to stress.   The tree editions of his book, Clinical Mechanics of the Hand, contain synopses of this research.  Although these books are out of print, they continue to be highly regarded and prized in the hand/upper extremity surgery and rehabilitation fields.  You may be able to locate one or all of these books through your medical library; or the Internet.  Below are a few of Brand's journal publications that may pertain:

Brand, P. W. (2006). Pressure sore--the problem. J Tissue Viability, 16(2), 9-11.

MacMoran, J. W., & Brand, P. W. (1987). Bone loss in limbs with decreased or absent sensation: ten year follow-up of the hands in leprosy. Skeletal Radiol, 16(6), 452-459.

Brand, P. W. (1979). Management of the insensitive limb. Phys Ther, 59(1), 8-12.

Brand, P. W., & Ebner, J. D. (1969). Pressure sensitive devices for denervated hands and feet. A preliminary communication. J Bone Joint Surg Am, 51(1), 109-116.

Brand, P. W., & Ebner, J. D. (1969). A pain substitute pressure assessment in the insensitive limb. Am J Occup Ther, 23(6), 479-486.

Thank You Rayudu Nithin manohar

Dear my  colleague Hamdy,

I hope the following papers help you.

Buller, P. F., & McEvoy, G. M. (2012). Strategy, human resource management and performance: Sharpening line of sight. Human Resource Management Review. http://doi.org/10.1016/j.hrmr.2011.11.002

Cholewicki, J., & McGill, S. M. (1996). Mechanical stability of the in vivo lumbar spine: Implications for injury and chronic low back pain. Clinical Biomechanics, 11(1), 1–15. http://doi.org/10.1016/0268-0033(95)00035-6

Colloca, C. J., Keller, T. S., Harrison, D. E., Moore, R. J., Gunzburg, R., & Harrison, D. D. (2006). Spinal manipulation force and duration affect vertebral movement and neuromuscular responses. Clinical Biomechanics, 21(3), 254–262. http://doi.org/10.1016/j.clinbiomech.2005.10.006

Danion, F., & Sarlegna, F. R. (2007). Can the human brain predict the consequences of arm movement corrections when transporting an object? Hints from grip force adjustments. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 27(47), 12839–12843. http://doi.org/10.1523/JNEUROSCI.3110-07.2007

Dulhunty, J. (2002). A simplified conceptual model of the human cervical spine for evaluating force transmission in upright static posture. Journal of Manipulative and Physiological Therapeutics, 25(5), 306–317. http://doi.org/10.1067/mmt.2002.124421

Flanagan, J. R., & Wing, a M. (1997). The role of internal models in motion planning and control: evidence from grip force adjustments during movements of hand-held loads. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 17(4),

Han, J. S., Goel, V. K., & Kumar, S. (1991). A nonlinear optimization force model of the human lumbar spine. International Journal of Industrial Ergonomics, 8(3), 289–301. http://doi.org/10.1016/0169-8141(91)90039-O

Hansen, L., de Zee, M., Rasmussen, J., Andersen, T. B., Wong, C., & Simonsen, E. B. (2006). Anatomy and biomechanics of the back muscles in the lumbar spine with reference to biomechanical modeling. Spine, 31(17), 1888–99.

Keenan, K. G., Santos, V. J., Venkadesan, M., & Valero-Cuevas, F. J. (2009). Maximal voluntary fingertip force production is not limited by movement speed in combined motion and force tasks. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 29(27), 8784–8789. http://doi.org/10.1523/JNEUROSCI.0853-09.2009

Lackner, J. R., & Dizio, P. (1994). Rapid adaptation to Coriolis force perturbations of arm trajectory. J Neurophysiol, 72(1), 299–313. http://doi.org/citeulike-article-id:450102

Ledet, E. H., Tymeson, M. P., DiRisio, D. J., Cohen, B., & Uhl, R. L. (2005). Direct real-time measurement of in vivo forces in the lumbar spine. Spine Journal, 5(1), 85–94. http://doi.org/10.1016/j.spinee.2004.06.017

Luinge, H. J., & Veltink, P. H. (2004). Inclination measurement of human movement using a 3-D accelerometer with autocalibration. IEEE Transactions on Neural Systems and Rehabilitation Engineering : A Publication of the IEEE Engineering in Medicine and Biology Society, 12(1), 112–121. http://doi.org/10.1109/TNSRE.2003.822759

Shirazi-Adl, A., & Parnianpour, M. (1993). Nonlinear response analysis of the human ligamentous lumbar spine in compression. On mechanisms affecting the postural stability. Spine, 18(1), 147–58. http://doi.org/10.1097/00007632-199301000-00021

Shirazi-Adl, A., Sadouk, S., Parnianpour, M., Pop, D., & El-Rich, M. (2002). Muscle force evaluation and the role of posture in human lumbar spine under compression. European Spine Journal, 11(6), 519–526. http://doi.org/10.1007/s00586-002-0397-7

Shum, G. L. K., Crosbie, J., & Lee, R. Y. W. (2005). Symptomatic and asymptomatic movement coordination of the lumbar spine and hip during an everyday activity. Spine, 30(23), E697–702. http://doi.org/10.1097/01.brs.0000188255.10759.7a

Teo, E. C., & Ng, H. W. (2001). Evaluation of the role of ligaments, facets and disc nucleus in lower cervical spine under compression and sagittal moments using finite element method. Medical Engineering and Physics, 23(3), 155–164. http://doi.org/10.1016/S1350-4533(01)00036-4

Vaz, G., Roussouly, P., Berthonnaud, E., & Dimnet, J. (2002). Sagittal morphology and equilibrium of pelvis and spine. European Spine Journal, 11(1), 80–87. http://doi.org/10.1007/s005860000224

Wada, O., Tateuchi, H., & Ichihashi, N. (2014). The correlation between movement of the center of mass and the kinematics of the spine, pelvis, and hip joints during body rotation. Gait and Posture, 39(1), 60–64. http://doi.org/10.1016/j.gaitpost.2013.05.030

Wu, B., Wang, C., Krug, R., Kelley, D. A., Xu, D., Pang, Y., … Zhang, X. (2010). 7T human spine imaging arrays with adjustable inductive decoupling. IEEE Transactions on Bio-Medical Engineering, 57(2), 397–403. http://doi.org/10.1109/TBME.2009.2030170

Regards, 

Abdel-Rahman

Bone fracture, post op immobilisation , DM , hyperlipidaemia, carotid plaque etc has the potentiality to develop thrombus and or embolus which can cause blockage of arteriole on their  circulating path way. We ophthalmologist often keep our patient in prone position following VR surgery. There is hardly any report  of CRAO following such procedure. I also agree w other responder that positioning of the patient has not caused CRAO. 

Many thanks for your help. Your comments have helped me a lot as I could not find that information anywhere.

The Book by David Tittertoin "Strapdown Inertial Navigation Technology" is a very usful reference for that kind of work also.

Thank You Abdul Aziz , Prasannah , Navid and Zdenka for your suggestions.

Based on the literature survey i found that most of the works on different bones have used Linear models for static studies and other constitutive models such as concrete damage plasticity, hyperfoam , power law models etc for dynamic studies.

Hi I Wayan Sadwika

Can you tell me your stage of model building. so that we can share the problems and the people who have built the model can guide us in the same.

I am stuck at creating the disc.

I am trying to extract the geometry from the mesh file and trying to loft it. But then I m not able to build the bulge of disc.

Is there any easy method to go about the same.

PFA the pictures of the process.

Hello, some articles that can help you, kings regards.

1. Arch Phys Med Rehabil. 1994 Aug;75(8):895-9.

Changes in cervicocephalic kinesthesia after a proprioceptive rehabilitation

program in patients with neck pain: a randomized controlled study.

Revel M(1), Minguet M, Gregoy P, Vaillant J, Manuel JL.

Author information:

(1)Laboratoire d'Explorations Fonctionnelles de l'Appareil locomoteur et

d'évaluation du handicap, Hôpital Cochin, Paris, France.

Head repositioning accuracy (HRA) after full range active motion was evaluated in

60 cervicalgic patients. The mean angular error was 7.7 degrees +/- 3.3 (mean +/-

SD) and 82% were outside a threshold value of 4.5 degrees. After randomization 30

patients followed a rehabilitation program based on eye-head coupling (RG) and 30

served as a control group (CG). At 10 week follow-up, a greater gain in HRA was

observed in the RG (2 degrees +/- 2.7, mean +/- SD) than in the CG (0 +/- 2.6,

mean +/- SD) (p = 0.005). Clinical parameters (pain, drug intake, range of

motion, and self assessed functional improvement) were also more improved in the

RG than in the CG. These data emphasize the role of a neck proprioception

alteration in chronic neck pain and suggest that a rehabilitation program based

on eye-head coupling should be included in most medical management of cervicalgic

patients.

PMID: 8053797 [PubMed - indexed for MEDLINE]

2. Arch Phys Med Rehabil. 1991 Apr;72(5):288-91.

Cervicocephalic kinesthetic sensibility in patients with cervical pain.

Revel M(1), Andre-Deshays C, Minguet M.

Author information:

(1)Department de Reeducation, Clinique de Rhumatologie, Hopital COCHIN, Paris,

France.

Head orientation in space makes use of multiple sensory afferents, among which

the cervical proprioceptive cues could play a predominant role. To quantify the

alteration of neck proprioception in patients with cervical pathology, we

proposed a test for the clinical evaluation of the ability to relocate the head

on the trunk after an active head movement, for 30 healthy subjects and 30

patients with cervical pain. The data demonstrated that this ability was

significantly poorer in the patient group, indicating an alteration in neck

proprioception. This test permits a discriminant classification of healthy and

sick subjects, justifies proprioceptive rehabilitation programs, and allows a

quantitative evaluation of their results.

3. Physiother Theory Pract. 2008 Sep-Oct;24(5):380-91. doi:

10.1080/09593980701884824.

Test-retest reliability of cervicocephalic relocation test to neutral head

position.

Pinsault N(1), Fleury A, Virone G, Bouvier B, Vaillant J, Vuillerme N.

Author information:

(1)Laboratoire TIMC-IMAG, UMR UJF CNRS 5525, Grenoble, France.

Considering the important role of the cervical joint position sense on control of

human posture and locomotion, accurate and reliable evaluation of neck

proprioceptive abilities appears of great importance. Although the

cervicocephalic relocation test (CRT) to the neutral head position (NHP) usually

is used for both research and clinical purposes, its test-retest reliability has

not been clearly established yet. The purpose of the present experiment was to 1)

evaluate the test-retest reliability of the CRT to NHP and 2) to determine the

number of trial recordings required to ensure reliable measurements. To this aim,

40 young healthy adults performed the CRT to NHP on two separate occasions. Ten

trials were performed for each rotation side. Absolute and variable errors,

processed along their horizontal, vertical, and global components, were used to

assess the cervical joint repositioning accuracy and consistency, respectively.

Mean difference between test and retest with 95% confidence interval, intraclass

correlation coefficient, and Bland and Altman graphs with limits of agreement

were used as statistical methods for assessing test-retest reliability. Results

show that the CRT to NHP when executed in its original form (i.e., 10 trials) has

a fair to excellent reliability (ICC ranged from 0.52 to 0.81 and from 0.49 to

0.77, for absolute and variable errors, respectively); the test-retest

reliability of this test increases as the number of trials used to establish

subject's repositioning errors increases; and using the mean of eight trials is

sufficient to ensure fair to excellent reliability of the measurements (ICC

ranged from 0.39 to 0.78 and from 0.44 to 0.78, for absolute and variable errors,

respectively).

PMID: 18821444 [PubMed - indexed for MEDLINE]

4. Ann Readapt Med Phys. 2008 May;51(4):257-62. doi: 10.1016/j.annrmp.2008.02.004.

Epub 2008 Apr 29.

[Impact of nociceptive stimuli on cervical kinesthesia].

[Article in French]

Vaillant J(1), Meunier D, Caillat-Miousse JL, Virone G, Wuyam B, Juvin R.

Author information:

(1)Centre de recherche et d'innovation en kinésiologie, kinésiopathologie et

kinésithérapie, institut universitaire professionnalisé en ingénierie de la

santé, BP 217, 38049 Grenoble cedex 09, France. JVaillant@chu-grenoble.fr

The goal of this study was to evaluate the impact of nociceptive stimuli upon the

cervical proprioception ability.METHOD: Thirty healthy young subjects performed a

cervicocephalic relocation test (CRT) in two random conditions: the first one was

based on a nociceptive electric stimulation called condition "pain", whereas the

second one was targeting a painless electric condition called condition

"control". The CRT consisted of repositioning the head on the trunk, after an

active transversal movement of the head in the transverse field with closed eyes.

The pointing was recorded at the beginning and at the end of each rotation using

a custom video acquisition system.

RESULTS: The average mean of error repositioning was worth 3.98+/-0.99 degrees

(average mean, standard deviation) in the condition "pain", and 1.75+/-0.37

degrees in the condition "control" (p<0.01).

CONCLUSION: Acute pain provokes a disturbance of the cervical proprioception

ability without damaging the anatomic structure. This observation suggests the

interest of an early follow-up of the pain to avoid sensory disturbances, as well

as the establishment of a cervical proprioceptive rehabilitation program after an

algic event.

5. Phys Ther Sport. 2010 May;11(2):66-70. doi: 10.1016/j.ptsp.2010.02.004. Epub 2010

Mar 15.

Cervical joint position sense in rugby players versus non-rugby players.

Pinsault N(1), Anxionnaz M, Vuillerme N.

Author information:

(1)Ecole de kinésithérapie du CHU de Grenoble, France; TIMC-IMAG laboratory AFIRM

and AGIM3 teams, UMR UJF-CNRS 5525, Grenoble, France. npinsault@chu-grenoble.fr

<npinsault@chu-grenoble.fr>

OBJECTIVE: To determine whether cervical joint position sense is modified by

intensive rugby practice.

DESIGN: A group-comparison study.

SETTING: University Medical Bioengineering Laboratory.

PARTICIPANTS: Twenty young elite rugby players (10 forwards and 10 backs) and 10

young non-rugby elite sports players.

INTERVENTIONS: Participants were asked to perform the cervicocephalic relocation

test (CRT) to the neutral head position (NHP) that is, to reposition their head

on their trunk, as accurately as possible, after full active left and right

cervical rotation. Rugby players were asked to perform the CRT to NHP before and

after a training session.

MAIN OUTCOME MEASUREMENTS: Absolute and variable errors were used to assess

accuracy and consistency of the repositioning for the three groups of Forwards,

Backs and Non-rugby players, respectively.

RESULTS: The 2 groups of Forwards and Backs exhibited higher absolute and

variable errors than the group of Non-rugby players. No difference was found

between the two groups of Forwards and Backs and no difference was found between

Before and After the training session.

CONCLUSIONS: The cervical joint position sense of young elite rugby players is

altered compared to that of non-rugby players. Furthermore, Forwards and Backs

demonstrated comparable repositioning errors before and after a specific training

session, suggesting that cervical proprioceptive alteration is mainly due to

tackling and not the scrum.

PMID: 20381004 [PubMed - indexed for MEDLINE]

6. Spine (Phila Pa 1976). 2010 Feb 1;35(3):294-7. doi: 10.1097/BRS.0b013e3181b0c889.

Degradation of cervical joint position sense following muscular fatigue in

humans.

Pinsault N(1), Vuillerme N.

Author information:

(1)AFIRM Team, TIMC-IMAG Laboratory, UMR UJF CNRS 5525, La Tronche, France.

STUDY DESIGN: Before and after intervention trials.

OBJECTIVE: To investigate the effect of cervical muscular fatigue on joint

position sense.

SUMMARY OF BACKGROUND DATA: Although fatigue-related degradation of

proprioceptive acuity at lower and upper limbs is well documented, to date no

study has investigated whether muscular fatigue induced at the neck could modify

joint position sense.

METHODS: A total of 9 young healthy adults were asked to perform the

cervicocephalic relocation test to the neutral head position, that is, to

relocate the head on the trunk, as accurately as possible, after full active

cervical rotation to the left and right sides. This experimental task was

executed in 2 conditions of No fatigue and Fatigue of the scapula elevator

muscles. Absolute and variable errors were used to assess the cervical joint

repositioning accuracy and consistency, respectively.

RESULTS: Less accurate and less consistent repositioning performances were

observed in Fatigue relative to No fatigue condition, as indicated by increased

absolute and variable errors, respectively.

CONCLUSION: Results of the present experiment evidence that cervical joint

position sense, assessed through the cervicocephalic relocation test to the

neutral head position, is degraded by muscular fatigue.

PMID: 20075783 [PubMed - indexed for MEDLINE]

7. Arch Phys Med Rehabil. 2008 Dec;89(12):2375-8. doi: 10.1016/j.apmr.2008.06.009.

Cervicocephalic relocation test to the neutral head position: assessment in

bilateral labyrinthine-defective and chronic, nontraumatic neck pain patients.

Pinsault N(1), Vuillerme N, Pavan P.

Author information:

(1)Laboratoire TIMC-IMAG, UMR CNRS 5525, Grenoble, France.

OBJECTIVE: To determine whether vestibular or cervical proprioceptive information

influence the cervicocephalic relocation test to the neutral head position, by

comparing head repositioning errors obtained in asymptomatic, unimpaired control

subjects with those obtained in bilateral labyrinthine-defective patients and

chronic, nontraumatic neck pain patients.

DESIGN: A group-comparison study.

SETTING: University medical bioengineering laboratory.

PARTICIPANTS: Labyrinthine-defective patients (n=7; mean age+/-SD, 67+/-15 y),

nontraumatic neck pain patients (n=7; 56+/-9 y), and asymptomatic, unimpaired

control subjects (n=7; 64+/-12 y).

INTERVENTIONS: Participants were asked to relocate the head on the trunk, as

accurately as possible, after full active cervical rotation to the left and right

sides. Ten trials were performed for each rotation side.

MAIN OUTCOME MEASURES: Absolute and variable errors were used to assess accuracy

and consistency of the repositioning, respectively.

RESULTS: No significant difference in repositioning errors was observed between

labyrinthine-defective patients and control subjects, whereas nontraumatic neck

pain patients demonstrated significantly increased absolute errors in horizontal

and global components and higher variable errors in horizontal component.

CONCLUSIONS: These findings suggest that the vestibular system is not involved in

the performance of the cervicocephalic relocation test to neutral head position,

and further support this test as a measure of cervical proprioceptive acuity.

PMID: 19061750 [PubMed - indexed for MEDLINE]

This article is very useful thx.

There are a suite of papers centred on the work of Mike Watt in NZ around the relationship between slenderness and MOE.

Watt, Michael S., et al. "Modelling environmental variation in Young's modulus for Pinus radiata and implications for determination of critical buckling height." Annals of botany 98.4 (2006): 765-775.

Lasserre, Jean-Pierre, et al. "Influence of initial planting spacing and genotype on microfibril angle, wood density, fibre properties and modulus of elasticity in Pinus radiata D. Don corewood." Forest Ecology and Management 258.9 (2009): 1924-1931.

There is also a lot of detailed within-tree variation in wood property data based around the SilviScan technology and the work of Rob Evans. e.g 

Medhurst, Jane, et al. "Intra-specific competition and the radial development of wood density, microfibril angle and modulus of elasticity in plantation-grown Eucalyptus nitens." Trees 26.6 (2012): 1771-1780.

Hi Ashley,

May I suggest you to use Scilab (http://www.scilab.org/), which is a FREE computation software initially  developed by the French Research Institute on Artificial Intelligence (INRIA) and which is quite as powerful as Matlab ?

Best regards,

Philippe.

I recall reading some of Neville Hogan's papers on joint stiffness which could be relevant and helpful to answering your question.  Those works were done at MIT in 80's.

To calculate norms, you want to use the most accurate scores. In your situation, these would be the scores from the experienced tester/s. If you have gross differences between testers, was this random error or systematic error (did you use 1-way or 2-way ICC? Did you compare means with an F-test?). One almost has to assume, barring evidence to the contrary, that disagreement was due to the inexperienced tester.

Was the highest ICC between the two more experienced testers? I'm not sure about your last question - what is HP-SFB?

Some informations in these publications.

Maximum torques vary between surfaces and wheelchair type.

Vasanth,

The marker placement strategy can certainly dictate how you determine the underlying skeletal kinematics.  There are, for example, the point cluster method by Andriacchi et al and minimum marker (less than 3 per body segment) method we developed which would use mathematical optimization to enforce the rigidity of underlying linkage.  Hope this helps.

Hi Arash, the following may give you some idea altough its quite difficult to determine the strain using ultrasound during dynamic activities such as running

J Exp Biol. 2014 Sep 1;217(Pt 17):3159-68. doi: 10.1242/jeb.100826. Epub 2014 Jun 19.

Tendon elastic strain energy in the human ankle plantar-flexors and its role with increased running speed.

Lai A1, Schache AG1, Lin YC1, Pandy MG2.

Cheers

Steve

Hi Angadkumar,

Here are some links that may help you with gait data on impaired and non-impaired individuals. On the Hicks and colleagues paper see the "Appendix A. Supplementary materials".

Good luck!

Hi Vahid

See if this can help you; multi-sensing data from a variety of daily activities:

Dear Mr Prakash,

Are you talking about a full gait analysis lab with 3D motion capture (for kinematics), forceplates (for dynamics), electromyography, etc..., or maybe only electronic walkway and/or video? Is it mainly a clinical gait lab or not?

The answers to your request (which material) will depend on your needs. Please give some more details. 

Regarding color background,

if you are using mocap system with passive markers (reflective), as Vicon system, better way is to try to reduce the reflectance value of your environment (i.e. laminate floor, walls), even if recent cameras and software allow to deal with reflectance. So a choice of matte color is surely better.

For the color itself, it depends your feelings. It only has to allow you to get a good color contrast for video quality purpose. You can look at usual choice of color in clinical settings and effect of colors on mood and behavior.

I hope this will help you in your thinking.

Best regards,

AG.

Hi Raul,

In addition to using such cameras, one can also utilize an accelerometer together with the tracker to measure speed and change in direction.

You can then also download and use the simple free software of Kinovea to see the analysis that you want.

Hope this helps.

Regards,

Habib

Hello Félix ,

I am a Master students and I am using that system for my research. As you say they are prone to drift and magnetic disturbances. 

I use them inside of a Human Motion Lab therefore magnetic disturbances are high. You are suppose to use the KiC no magnetometers when you expect non-homogeneus disturbances, however switching to that fusion engine didn´t help much because then drift is worse than magnetic distortions. One way around it is to find a "magnetically safe" spot and calibrate it always there. I find the need to calibrate them on top of  a 40cm high wooden step to avoid disturbances from the ground.

That is the main issue I've found. If you use them outside the lab it might get better, I cannot give you feedback about that. 

Regarding the wireless system I haven't had problems with that. I record about 3hours per experiment but I do notice that instabilities are  more likely to occur when battery is about 50%, so you kind of have to hurry up with your experiments.

I think it is a usefull technology, once you find the tricks to make the best out of it. 

I hope this is helpful.