The Foot Posture Index in the Clinical Assessment of the Foot - Essay Example

The foot is made up of twenty six bones, making it a very flexible entity. However, because of the foot’s complexity, large deformations can also occur from its posture, and these deformations can place huge amounts of both strain and stress on an individuals’ foot, creating much pain and discomfort for some individuals (Ho, 1998). Therefore, a three dimensional shape creation of the foot is very important when it comes to designing a shoe, or a good protective covering for the foot (Johnson, R. A., & Wichern, D. W., 1992). One can use anthropometric dimensions to predict certain concepts about the foot, and this can be enhanced by using a three dimensional model and approach to the foot (Choie, 1991). Advancements in technology and computers have allowed many to consider replacing the expert last marker with some type of computer design. Some individuals have created presentations of the foot in third-dimensional designs using Bezier curves (Lord, M., Foulston, J., & Smith, P. J., 1991). These types of models can help individuals in the grazing and sizing areas. Yavatkar (1993) created a model of a person’s foot using I-DEAS graphics and programming. He was able to create this third dimensional model of the foot by lofting four segments (forefoot to instep, instep to medial-lateral malleolus level, malleolus to lateral point of heel, and heel part) within the foot. One could determine the accuracy of this model by taking a look at the outside and indsie of the foot form. The volume outside was around 50 000 [mm.sup.3], whereas the amount within the foot was approximately 27 000 [mm.sup.3]. However, whether one can use this model to generalize is not efficiently known overall since he only used one foot for the model (DeBor, 1978). It is known, however, that aspects of computer-aided design have been used in order to make orthopedic shoe uppers as well as some custom footwear (Houle, Beaulie, and Liu, 1998). In order to make the approach to foot measuring more efficient, B-spline surface modeling techniques, regression and recursive methods to make a basic third dimensional foot model using basic anthropometric measures can be used. B-splines can be studied in order to create an example for using within the sphere of computer-aided design software. (Choi, 1991). Within this concept. B-splines are generally considered as the best curve or surface representation. This is usually because they have a very helpful set of properties, which include continuity, local shape controllability, spatial uniqueness, and boundedness (Bartles, Beatty and Barsky, 1987). Furthermore, the use of recursive algorithms produce the ability to solve very complex issues and problems by using efficient and effective algorithms (Roberts, 1986). Furthermore, regression methods are often used to make a polynomial equation between variables that consist of two or more items. Once one generates the foot shape, the difference between the predicted shape and the real foot shape can be created using various features of color coding. These measures may greatly benefit the sizing of feet (Freedman, A., Huntington, E. C., Davis, G. C., Magee, R. B., Milstead, V. M., & Kirkpatrick, C. M., 1946). In a recent study used for foot sizing, Houle, Beaulie, and Liu, (1998) used 65 Hong Kong Chinese Men to see if this concept would work well. 40 of these men were in the group to generate the prediction model, and the other 25 participated in making the validated model. None of the participants reported any foot diseases or abnormality. The participants recorded their age, weight, and height on a consent form. They then washed their feet, and their feet were measured using the Brannock device while they stood on both feet. Eleven different points were marked on the foot. According to the researchers: “7 on the metatarsalphalangeal joint (MPJ), 2 on the dorsal surface of foot (Points 8 and 9), and 2 on the plantar side of the foot (Points 10 and 11). The last 4 points were chosen so that each scanned foot could be aligned during processing (see sections titled Metatarsal-Phalangeal Joint Equations and "Standard" Foot and footShape Prediction Model). The MPJ points were used so that the MPJ line could be modeled using polynomial regression (see section titled Metatarsal-Phalangeal Joint Equations)” (Houle, Beaulie, and Liu, 1998, 119). The results of the study by Houle, Beaulie, and Liu (1998) attempted to tell if a parametric approach to foot shape predication could be achieved. According to the researchers: “The results show that the foot shape may be predicted to a mean accuracy of 2.1 mm for the left foot and 2.4 mm for the right foot. The prediction model may be improved by using more participants. However, different standard feet may be necessary to account for differences in age, gender, race, and so on” (Houle, Beaulie, and Liu, 1998, 129). It may also be important to consider that fact that different standards of feet may also be developed as more studies are done to account for various factors such as age, gender, race, and other items. If we assume that the shape of the foot for standardization is known, using some of the anthropometric dimensions can help to ascertain the third dimensional foot shape in an individual person. It can be argued that the predicted foot shape may allow when to formulate a personal, customized shoe with a limited number of dimensions. The researchers did notice that there were higher negative errors “around the medial and lateral malleolus. These errors are most probably attributable to movement or change in a participants posture while standing during scanning. This error may be reduced if the participants posture during measurement can be controlled effectively.” (Houle, Beaulie, and Liu, 1998, 131). There were also other errors present in the society. For instance, the width at the heel region seemed to have the higher number or errors. This could have been because the actual width calculation was focused on the toe region itself. According to the researchers: “The error may be reduced if both forward (from toe) and backward (from heel) calculations are performed until the results converge within a specific tolerance. The backward calculation can consider a seed section around the heel and thus will counteract the effect of the forward prediction, thereby reducing the error and eventually improving the prediction accuracy” ((Houle, Beaulie, and Liu, 1998, 135). There was also another error to consider, which was a positive error when one attempted to predict the height. The model used different foot sections taken at a fixed percentage of the desired length of the foot (Goonetilleke, R. S., & Luximon, A., 1999). Differences in foot shape and sections must be considered to appear at a given percentage in people. Therefore, an improvement in this type of measure could be made by attempting to find the best measures and matches for the different sections of the feet, rather than with the foot as a whole. Also, it may be worthwhile to note that different ways of scaling the foot could also be used if various types of feet need to be categorized and measured. Therefore, anthropometric foot measures can be improved upon by using four of their measures (length, width, height and curvature) to attempt to predict the third dimensional shape of a foot. These measures can be used across 98 different cross sections of the foot, as they were in the study, to produce effective results (Bartels, R. H., Beatty, J. C., & Barsky, B. A., 1987). It is hoped that using measures like this to better measure the foot will result in more comfortable foot protection for many individuals that suffer from pain based on their various foot issues. Bartels, R. H., Beatty, J. C., & Barsky, B. A. (1987). An introduction to splines for use in computer graphics and geometric modeling. San Francisco: Morgan Kaufmann. Choi, B. K. (1991). Surface modeling for CAD/CAM: Advances in industrial engineering (Vol. 11). Amsterdam: Elsevier. DeBoor, C. (1978). A practical guide to splines. New York: Springer-Verlag. Freedman, A., Huntington, E. C., Davis, G. C., Magee, R. B., Milstead, V. M., & Kirkpatrick, C. M. (1946). Foot dimensions of soldiers (Third Partial Report, Project No. T-13). Fort Knox, KY: Armored Medical Research Laboratory. Goonetilleke, R. S., & Luximon, A. (1999). Foot flare and foot axis. Human Factors, 41, 596-606. Ho, C.-F. (1998). 3-Dimensional foot digitization. Unpublished masters thesis, Department of Industrial Engineering and Engineering Management, Hong Kong University of Science and Technology, Hong Kong. Houle, P.-S., Beaulieu, E., & Liu, Z. (1998). Novel fully integrated computer system for custom footwear: From 3D digitization to manufacturing. In Proceedings of the International Society for Optical Engineering: Three-dimensional image capture and applications (pp. 65-73). San lose, CA: SPIE--The International Society for Optical Engineering. Johnson, R. A., & Wichern, D. W. (1992). Applied multivariate statistical analysis. Englewood Cliffs, NJ: Prentice-Hall. Lord, M., Foulston, J., & Smith, P. J. (1991). Technical evaluation of a CAD system for orthopaedic shoe-upper design. Journal of Engineering in Medicine, 205(H2), 109-115. Yavatkar, A. S. (1993). Computer aided system approach to determine the shoe-last size and shape based on statistical approximated model of a human foot. Unpublished masters thesis, Tufts University, Medford, MA. Read More