Biomedical subjects

Ravindra S Goonetilleke

Publications and source records attributed to Ravindra S Goonetilleke.

4 recordsLinked to original sources

PubMed2005

3D foot shape generation from 2D information.

Two methods to generate an individual 3D foot shape from 2D information are proposed. A standard foot shape was first generated and then scaled based on known 2D information. In the first method, the foot outline and the foot height were used, and in the second, the foot outline and the foot profile were used. The models were developed using 40 participants and then validated using a different set of 40 participants. Results show that each individual foot shape can be predicted within a mean absolute error of 1.36 mm for the left foot and 1.37 mm for the right foot using the first method, and within a mean absolute error of 1.02 mm for the left foot and 1.02 mm for the right foot using the second method. The second method shows somewhat improved accuracy even though it requires two images. Both the methods are relatively cheaper than using a scanner to determine the 3D foot shape for custom footwear design.

Adult↗

PubMed2004

Dimensional differences for evaluating the quality of footwear fit.

Very few standards exist for fitting products to people. Footwear is a noteworthy example. This study is an attempt to evaluate the quality of footwear fit using two-dimensional foot outlines. Twenty Hong Kong Chinese students participated in an experiment that involved three pairs of dress shoes and one pair of athletic shoes. The participants' feet were scanned using a commercial laser scanner, and each participant wore and rated the fit of each region of each shoe. The shoe lasts were also scanned and were used to match the foot scans with the last scans. The ANOVA showed significant (p < 0.05) differences among the four pairs of shoes for the overall, fore-foot and rear-foot fit ratings. There were no significant differences among shoes for mid-foot fit rating. These perceived differences were further analysed after matching the 2D outlines of both last and feet. The point-wise dimensional difference between foot and shoe outlines were computed and analysed after normalizing with foot perimeter. The dimensional difference (DD) plots along the foot perimeter showed that fore-foot fit was strongly correlated (R(2) > 0.8) with two of the minimums in the DD-plot while mid-foot fit was strongly correlated (R(2) > 0.9) with the dimensional difference around the arch region and a point on the lateral side of the foot. The DD-plots allow the designer to determine the critical locations that may affect footwear fit in addition to quantifying the nature of misfit so that design changes to shape and material may be possible.

Adult↗

PubMed2004

Foot shape modeling.

This study is an attempt to show how a "standard" foot can be parameterized using foot length, foot width, foot height, and a measure of foot curvature so that foot shape can be predicted using these simple anthropometric measures. The prediction model was generated using 40 Hong Kong Chinese men, and the model was validated using a different group of 25 Hong Kong Chinese men. The results show that each individual 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. Application of this research includes the potential design and development of custom footwear without the necessity of expensive 3-D scanning of feet.

Adult↗

PubMed2003

Foot landmarking for footwear customization.

As consumers are becoming increasingly selective of what they wear on their feet, manufacturers are experiencing problems developing and fitting the right footwear. Literature suggests that shoes with a shape similar to feet may be comfortable because they attempt to maintain the feet in a neutral posture. The objective of this paper is to develop a metric to quantify mismatches between feet and lasts and also to be able to generate the two-dimensional outline of the foot using the minimum number of landmarks. Fifty Hong Kong Chinese were participants in the experiment. In addition to subject weight, height, foot length and foot width, the left foot outlines were drawn and 18 landmarks were marked on each of the two-dimensional foot outlines. A step-wise procedure was used to reduce the chosen 18 landmarks to eight, such that the mean absolute negative error (an indicator of 'tightness') between the foot outline and the modelled curve was 1.3 mm. These eight landmarks seem to show an improvement over those proposed by other researchers, thus showing the importance of choosing the right landmarks for modelling the foot. The positive and negative absolute errors were on average 1.8 mm and 1.3 mm respectively. Moreover, the mean errors for the toe region and for the rest of the foot were 1.7 mm and 1.6 mm respectively. The results indicate that the foot outline, an important component for footwear functionality and fitting, may be modelled using eight critical landmarks.

Adult↗