-
Role of Helmet Fit on Angular and Linear Accelerations of Head in Ice Hockey
Hesam Sarvghad Moghaddam,
Whitman Kwok
Issue:
Volume 7, Issue 2, June 2019
Pages:
26-32
Received:
6 August 2019
Accepted:
23 August 2019
Published:
6 September 2019
Abstract: Increasing the protection efficiency of helmets is counted as the biggest challenge in ice hockey. The main objective of this study is twofold: first to understand the effect of fitting on the protection capability of ice hockey helmets, and second to determine a possible optimal fit with respect to minimum head accelerations. A purpose-built monorail drop tower was utilized to perform front and front boss impacts at a velocity of 4.47m/s on a custom headform outfitted with a commercial helmet (CCM Resistance) with no gap (tight fit), 2mm (regular fit), and 5 mm gaps (loose fit). It was observed that while in both impacts linear accelerations were lower for the regular fit model, the loose fit model predicted the lowest angular accelerations. A loosely-fitted helmet provides non-deterministic shifting upon impact which generally leads to a wider standard deviation of linear and angular accelerations. The results indicated that in front impacts while introducing a gap reduced the risk of focal injuries, only the loose fit model suggested lower risks of concussive injuries. However, the regular and loose fit models showed better protection against focal and concussive injuries in the front boss impacts, respectively.
Abstract: Increasing the protection efficiency of helmets is counted as the biggest challenge in ice hockey. The main objective of this study is twofold: first to understand the effect of fitting on the protection capability of ice hockey helmets, and second to determine a possible optimal fit with respect to minimum head accelerations. A purpose-built monor...
Show More
-
Using Data Mining Algorithms for Thalassemia Risk Prediction
Ngozi Chidozie Egejuru,
Sekoni Olayinka Olusanya,
Adanze Onyenonachi Asinobi,
Omotayo Joseph Adeyemi,
Victor Oluwatimilehin Adebayo,
Peter Adebayo Idowu
Issue:
Volume 7, Issue 2, June 2019
Pages:
33-44
Received:
7 August 2019
Accepted:
23 August 2019
Published:
6 September 2019
Abstract: This study predict the risk of thalassemia in all age groups based on identified risk of thalassemia. Knowledge about the risk factors for thalassemia was identified using structural interview with experienced medical personnel and questionnaire which was used to collect empirical medical database on the parameters. Supervised machine learning algorithms was used to formulate the predictive model for risk of thalassemia using the parameters and data identified and collected. The predictive model for the risk of thalassemia was simulated using the Waikato Environment for Knowledge Analysis (WEKA). The simulated model was validated using the historical data collected from the hospitals explaining the parameters and the risk of Thalassemia. The results of the study showed that following the collection of data from 51 patients, the parameters identified included demographic variables like gender, age, marital status, ethnicity and social class while the clinical variables included family history, spleen enlargement, diabetes, urine colour changes and parent carriers while the distribution of the risk was 43% no cases, 10% low cases, 16% moderate cases and 31% high cases. The study concluded that using the multi-layer perceptron for the prediction of Thalassemia will improve the decision making process within the healthcare service concerning Thalassemia.
Abstract: This study predict the risk of thalassemia in all age groups based on identified risk of thalassemia. Knowledge about the risk factors for thalassemia was identified using structural interview with experienced medical personnel and questionnaire which was used to collect empirical medical database on the parameters. Supervised machine learning algo...
Show More
-
Analysis on Image Processing Technology-based Diabetic Retinopathy
Issue:
Volume 7, Issue 2, June 2019
Pages:
45-60
Received:
10 August 2019
Accepted:
29 August 2019
Published:
16 September 2019
Abstract: Diabetic retinopathy is a dangerous eye disease which causes the blindness widely in the human society. It arises due to high sugar level in the blood. The eye disease caused due to the diabetes is called diabetic retinopathy. The symptoms of diabetic retinopathy are red lesions such as microaneurysms (MA), intraretinal hemorrhages and bright lesions such as exudates, cotton wool spots and blood vessels. Microaneurysm (MA) is one of the features of the diabetic retinopathy. They are discrete, localized saccular distensions of the weakened capillary walls and appear as small round dark red dots on the retinal surface. According to the medical definition of MA, it is a reddish, circular pattern with a diameter λ is less than 125μm. Microaneurysms are mostly found near thin blood vessels, but cannot actually be located on the blood vessels. According to the number of microaneurysms, the diabetic retinopathy can be classified as mild stage, moderate stage and the severe stage. To detect the microaneurysms, the optic disc and the blood vessels are firstly detected because they are normal features of the image. And also, in the detection of microaneurysms the pre-processing stage is very important. In the pre-processing stage, the filtering techniques and the histogram equalization techniques are applied to reduce the effect of noise and uneven illumination cases. To detect the optic disc, blood vessels and the microaneurysms, the mathematical morphological method is applied. The result of this research can help in the screening of diabetic retinopathy.
Abstract: Diabetic retinopathy is a dangerous eye disease which causes the blindness widely in the human society. It arises due to high sugar level in the blood. The eye disease caused due to the diabetes is called diabetic retinopathy. The symptoms of diabetic retinopathy are red lesions such as microaneurysms (MA), intraretinal hemorrhages and bright lesio...
Show More
