This study explores plantar pressure distribution (PPD) during walking in three groups: elite Alp... more This study explores plantar pressure distribution (PPD) during walking in three groups: elite Alpine Skiers (Alp), Cross-Country Skiers (XC), and Non-Athletes (NA). Using the emed c50 platform, PPD parameters were measured, and ANOVA with post hoc Tukey tests conducted. Notable variations in vertical ground reaction forces (VGRF) and peak pressures emerged. Alp had lower VGRF in lateral heel, hallux, and metatarsals (third to fth), with higher force in the second metatarsal than XC. Alp's peak pressure decreased in lateral heel and hallux but increased in lateral midfoot and third to fth toes compared to XC and NA. Alp displayed smaller contact areas in certain regions (lateral heel, medial midfoot, and metatarsals third to fth), with larger areas in medial midfoot and third and fth toes. Alp had reduced ground contact time in the second metatarsal compared to NA. Center of Pressure (COP) displacement analysis revealed distinct patterns. Alp had reduced heel displacement, XC less midfoot displacement. Both Alp and XC showed shorter COP displacement times in midfoot compared to NA. In conclusion, despite the consistent arch index, these ndings underscore the profound impact of skiing Activities, especially in Alp, on various biomechanical parameters. This research holds signi cance for foot health assessment, motion optimization, athlete differentiation, and the development of targeted training strategies in Alpine and Cross-Country skiing
According to the content-based image retrieval system, this system has low retrieval accuracy pro... more According to the content-based image retrieval system, this system has low retrieval accuracy problems and high computational complexity. A novel method for content-based image retrieval system, proposed in this paper based on Color and gradient feature. Color-histogram and Color-moment are used for Color feature and as its gradient feature, edge histogram feature (SRF) is adopted based on the Local Binary Pattern. Besides the use of the above mentioned features, the Principle Component Analysis has been used for decreasing the dimension of features. After performing PCA upon each feature, the weight was assigned to the Color feature and gradient feature, and similarity was also calculated based on normalizing Canberra Distance. Experimental results showed that in the proposed method "retrieval accuracy" is higher than conventional methods.
In this paper, a classifier based on a combination of Rough Set and 1-v-1 (one-versus-one) Suppor... more In this paper, a classifier based on a combination of Rough Set and 1-v-1 (one-versus-one) Support Vector Machine for Content Based Image Retrieval system is presented. Some problems of 1-v-1 Support Vector Machine can be reduced using Rough Set. With Rough Set, a 1-v-1 Support Vector Machine can provide good results when dealing with incomplete and uncertain data and features. In addition, boundary region in Rough Set can reduce the error rate. Storage requirements are reduced when compared to the conventional 1-v-1 Support Vector Machine. This classifier has better semantic interpretation of the classification process. We compare our Content Based Image Retrieval system with other image retrieval systems that uses neural network, K-nearest neighbour and Support Vector Machine as the classifier in their methodology. Experiments are carried out using a standard Corel dataset to test the accuracy and robustness of the proposed system. The experiment results show the proposed method can retrieve images more efficiently than other methods in comparison.
This study explores plantar pressure distribution (PPD) during walking in three groups: elite Alp... more This study explores plantar pressure distribution (PPD) during walking in three groups: elite Alpine Skiers (Alp), Cross-Country Skiers (XC), and Non-Athletes (NA). Using the emed c50 platform, PPD parameters were measured, and ANOVA with post hoc Tukey tests conducted. Notable variations in vertical ground reaction forces (VGRF) and peak pressures emerged. Alp had lower VGRF in lateral heel, hallux, and metatarsals (third to fth), with higher force in the second metatarsal than XC. Alp's peak pressure decreased in lateral heel and hallux but increased in lateral midfoot and third to fth toes compared to XC and NA. Alp displayed smaller contact areas in certain regions (lateral heel, medial midfoot, and metatarsals third to fth), with larger areas in medial midfoot and third and fth toes. Alp had reduced ground contact time in the second metatarsal compared to NA. Center of Pressure (COP) displacement analysis revealed distinct patterns. Alp had reduced heel displacement, XC less midfoot displacement. Both Alp and XC showed shorter COP displacement times in midfoot compared to NA. In conclusion, despite the consistent arch index, these ndings underscore the profound impact of skiing Activities, especially in Alp, on various biomechanical parameters. This research holds signi cance for foot health assessment, motion optimization, athlete differentiation, and the development of targeted training strategies in Alpine and Cross-Country skiing
According to the content-based image retrieval system, this system has low retrieval accuracy pro... more According to the content-based image retrieval system, this system has low retrieval accuracy problems and high computational complexity. A novel method for content-based image retrieval system, proposed in this paper based on Color and gradient feature. Color-histogram and Color-moment are used for Color feature and as its gradient feature, edge histogram feature (SRF) is adopted based on the Local Binary Pattern. Besides the use of the above mentioned features, the Principle Component Analysis has been used for decreasing the dimension of features. After performing PCA upon each feature, the weight was assigned to the Color feature and gradient feature, and similarity was also calculated based on normalizing Canberra Distance. Experimental results showed that in the proposed method "retrieval accuracy" is higher than conventional methods.
In this paper, a classifier based on a combination of Rough Set and 1-v-1 (one-versus-one) Suppor... more In this paper, a classifier based on a combination of Rough Set and 1-v-1 (one-versus-one) Support Vector Machine for Content Based Image Retrieval system is presented. Some problems of 1-v-1 Support Vector Machine can be reduced using Rough Set. With Rough Set, a 1-v-1 Support Vector Machine can provide good results when dealing with incomplete and uncertain data and features. In addition, boundary region in Rough Set can reduce the error rate. Storage requirements are reduced when compared to the conventional 1-v-1 Support Vector Machine. This classifier has better semantic interpretation of the classification process. We compare our Content Based Image Retrieval system with other image retrieval systems that uses neural network, K-nearest neighbour and Support Vector Machine as the classifier in their methodology. Experiments are carried out using a standard Corel dataset to test the accuracy and robustness of the proposed system. The experiment results show the proposed method can retrieve images more efficiently than other methods in comparison.
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