Exercise promotes health in people with diabetes. Weight-bearing activities increase the risk of foot ulcers. Air-pressure shoes may relieve high plantar pressure. Nevertheless, no study has investigated whether air-pressure shoes affect the plantar foot. A repeated measures study design, with five healthy subjects were tested with three inner air pressures (80, 160, and 240 mmHg) and 20 minutes of walking to examine their effects on peak plantar pressure (PPP). PPP after walking was measured from the forefoot in the big toe (T1), first metatarsal head (M1), and second metatarsal head (M2). We used a one-way ANOVA to analyze the results. We found that after walking for 20 min, inner air pressure significantly affected plantar pressure in the M1 and M2 (P = 0.008 and 0.006, respectively). Regarding the inner air pressure effect, there was a significant difference in the M1 head between 80 and 240 mmHg (274.2 ± 35.6 kPa vs. 689.4 ± 106.3 kPa, P = 0.002) at 20 minutes of walking duration. Moreover, there was a significant difference in the M2 head between 80 mmHg and 240 mmHg (250.6 ± 30.1 kPa vs. 572.4 ± 87.3 kPa, P = 0.002) and 160 and 240 mmHg (396.6 ± 35.3.7 kPa vs. 572.4 ± 87.3 kPa, P = 0.050). This finding is significant because the higher inner air pressure shoes can increase plantar pressure compared to 80 mmHg inner air pressure. This study suggests that individuals who are at high risk of developing foot ulcers should wear shoes with an inner air insole (80 mmHg).
Alzheimer's disease (AD) is a major public health priority. Hippocampus is one of the most affected areas of the brain and is easily accessible as a biomarker using MRI images in machine learning for diagnosing AD. In machine learning, using entire MRI image slices showed lower accuracy for AD classification. We present the select slices method by landmarks on the hippocampus region in MRI images. This study aims to see which views of MRI images have higher accuracy for AD classification. Then, to get the value of three views and categories, we used multiclass classification with the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset using Resnet50 and LeNet. The models were used in a total dataset of 4,500 MRI slices in three views and categories. Our study demonstrated that the selecting slices performed better than using entire slices in MRI images for AD classification. Our method improves the accuracy of machine learning, and the coronal view showed higher accuracy. This method played a significant role in improving the accuracy of machine learning performance. The results for the coronal view were similar to the medical experts usually used to diagnose AD. We also found that LeNet models became the potential model for AD classification.
Preventing diabetic foot ulcers (DFU) is critical for diabetes mellitus (DM) patients. Increased stiffness of plantar foot may cause higher plantar pressure leading to a higher risk of DFU. Soft tissue stiffness can be determined by measuring the soft tissue thickness with indentation depth and stress. Therefore, we hypothesized that the deep learning model could detect the ultrasound image pixel change under soft tissue compression. This study aimed to apply the deep learning model to analyze the ultrasound image pixel thickness of plantar foot, then predict the soft tissue indentation depth and loading force for estimating the stiffness. This study has developed a motor-driven ultrasound indentation system to apply programmable compression and simultaneously assess soft tissue mechanical properties and responses in indentation depth and loading force. In addition, the effective Young's modulus was calculated to characterize mechanical properties of soft tissues in the first metatarsal head. The deep learning method employed the YOLOv5x model to train and detect the small object in the indentation depth, such as ultrasound image pixel changes. Finally, the dataset images were processed with labeling annotation from the soft tissue indentation depth and loading force. The deep learning results showed 0.995 in mean Average Precision (mAP), 0.999 in precision, 1.000 in Recall, and 0.013 in Loss. A significant correlation was found between the ultrasound image pixel changes and soft tissue indentation depth (r = 0.98, p < 0.05). Furthermore, a significant correlation was observed between the ultrasound image pixel changes and the loading force in the first metatarsal head (r = 0.85, p < 0.05). The validation and prediction models were lower than the training models in the effective Young's modulus results. However, the results of the initial modulus were similar between the three models. Our findings recommend that applying deep learning in the ultrasound image can predict soft tissue indentation depth and loading force to calculate the stiffness of the plantar foot.
Layout variation is an essential concept in design and allows designers to create a sense of depth and complexity in their work. However, manually creating layout variations can be time-consuming and limit a designer's creativity. The use of generative art as a tool for creating visual poster designs that emphasize layout variety is explored in this study. Deep learning through generative art offers a solution by using an algorithm to generate layout variations automatically. This paper uses the VQGAN and CLIP approach to describe a generative art system, which renders images via a text prompt and produces a series of variations based on the zoom parameter 0.95 and shifts the y-axis 5 pixels. Our experiment shows that one frame can be generated roughly in 10.108±0.226 seconds, significantly faster than the conventional method for creating layouts on poster design. The model achieved a good quality image, scoring 4.248 using an inception score evaluation. The layout variations can be used as a basis for poster design visuals, allowing designers to explore different visual representations of layouts. This paper demonstrates the potential of generative art to explore layout variation in visual design, offering designers a new approach to creating dynamic and engaging visual designs.
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