Anti-Allergic App Features
The daily routine of billions of lives around the world depend upon how they wish to eat and stay fit. However, every human being are not born with the luck to eat whatever he or she want. There are obese people, who have to control their diet (Zarnowiecki et al. 2012). Then there are allergic people. To avoid allergic contamination, individuals are needed to be cautious of what he or she eats and the quantity of it (Sicherer and Sampson 2014). Information technology is crucial for human health in the modern generation (Jones et al. 2014). In the prospect to warning people off their allergies, the Anti-Allergic app helps a great deal.
The Anti-Allergic application is available for both android and IOS mobile platforms. This app is the common person’s guide to find eating joints that serve food according to their allergic intolerance. They surf through their database to present the user with the best restaurants that cater for their food allergies. The app developers believes in the concept of hassle free eating. This app gives the customers the choice to select their food preferences, measure the level of allergies they behold and then visit them with confidence.
A user after downloading the application from their respective app stores first requires signing up into the system. This application provides quite a handsome number of features to enhance user health and experience. The information processing system of the application aims to provide allergy free eating experience, right at the user’s fingertips. Users need to securely sign in with their personal details like name, password, contacts, and food preferences and so on. Health information security is important (Agaku et al. 2013). Users are also given the option to login into the system without an account. However, this will not save their food preferences.
Once the user has logged in, they are allowed to select what they are allergic to, from a list of graphic images of the most common allergens. They can also enter other allergens that do not appear in the provided list. This data then is saved in the app’s server database, for processing references.
Once the user has saved their allergic boundaries, the application can finally start its duty. The application surfs through its server database to present the user with all restaurants and or food joints that cater food according to the allergic constraints. This section is divided into three categories based on the distance of the restaurants from the user’s location. Restaurant names and their respective location is shared with the customer based the 1 mile, 5 miles and 10 miles distance radius. The application uses the GPS feature of the mobile to spot the user’s location and calculate distances accordingly (Cetateanu and Jones 2016). Here, the customer choses a particular restaurant as per their wish. This takes them into the particular restaurant’s Anti-Allergic portal. The grouped menu from the restaurant’s account is then displayed. Users are required to click on one of the menu groups to view the items that they cater under each. The system also counts the number of specific menus that these groups can cater abiding by the allergic constraints. On clicking each item, the user can view the allergens that these food items contain. Here is where, the application’s information gathering helps a great zeal. The server has direct connection to the chef’s kitchen. Here, the restaurants are liable towards updating the database with the ingredients of each item that they cater for the day. The application surfs through this, analyses the data to filter out the menus that the user is allergic to and displays the rest.
How the Anti-Allergic App Works
Users of the apps can also upload pictures of food or write in text the names of foods, to check the allergens that these items generally contain. Image processing and bar-code reading is a major aspect of this application. The users are given the ability to scan barcodes from the food packets to gather information about the ingredients that it contain (Pagoto et al. 2013). The application right away aware them off any allergen. In addition, the restaurants are rated and reviewed by the customers. This data is used by the Big Data analytic system to generate popularity index for the restaurants.
The application requires the mobile’s screen keyboard to take user input of the required data and search elements. In addition to that, the mobile camera helps to take pictures of respective foods or restaurants and upload them on the app. It is always recommended to have a high quality camera that can take clear images to scan bar codes off the food packages for better image processing (Sonka, Hlavac and Boyle 2014). The camera records the bar codes from the food packets and decoding is done inside the CPU according to the underlying software technologies extract information from each of the PD147 codes. The mobile GPS system helps in finding and analyzing location or distances of food joints. However, the mobile internet connection is always necessary.
The mobile screen is basically the only output medium of the application. The outputs from the CPU are primarily stored inside the RAM. The RAM generates Red, Green and Blue codes to determine the color and position of display output. Once this data is sent out to display, the RAM sometimes empties itself to ready itself for more. The RAM forms the bridge between the processor and graphics unit. From here, the images are flashed onto the screen at required intervals. More is the RAM size and the buffering speed, faster can the CPU dump its outputs into it. Figure below shows the working of the RAM, a dual core CPU and the display unit.
As for storage, the application uses the device’s internal memory to store the login details of the user. Furthermore, all other work is done off the online database server of the company. The app requires about 12 MB of on-installation storage memory. Further, it stores its necessary data as per the processing circumstances.
Being allergic to a certain food items, I myself gained ample help while researching and using this application. I decided to go with this application to know more about the use of information technology in health and hygiene. I got the opportunity to surf through a wide range of internet resources and research materials.
Nevertheless, time was a demeaning factor for me to compile my project. I would have loved to spend more time with the application and experience more features of it, but I was held back by time. This is a lesson that I take forward from this assignment.
I also plan to use the mobile device processing knowledge that I gained through this assignment in the coming future, to cater me more knowledge on information systems.
References
Agaku, I.T., Adisa, A.O., Ayo-Yusuf, O.A. and Connolly, G.N., 2013. Concern about security and privacy, and perceived control over collection and use of health information are related to withholding of health information from healthcare providers. Journal of the American Medical Informatics Association, 21(2), pp.374-378.
Cetateanu, A. and Jones, A., 2016. How can GPS technology help us better understand exposure to the food environment? A systematic review. SSM-population health, 2, pp.196-205.
Jia, M.Y., Wu, Q.S., Li, H., Zhang, Y., Guan, Y.F. and Feng, L., 2015. The calibration of cellphone camera-based colorimetric sensor array and its application in the determination of glucose in urine. Biosensors and Bioelectronics, 74, pp.1029-1037.
Jones, S.S., Rudin, R.S., Perry, T. and Shekelle, P.G., 2014. Health information technology: an updated systematic review with a focus on meaningful use. Annals of internal medicine, 160(1), pp.48-54.
Pagoto, S., Schneider, K., Jojic, M., DeBiasse, M. and Mann, D., 2013. Evidence-based strategies in weight-loss mobile apps. American journal of preventive medicine, 45(5), pp.576-582.
Sicherer, S.H. and Sampson, H.A., 2014. Food allergy: epidemiology, pathogenesis, diagnosis, and treatment. Journal of Allergy and Clinical Immunology, 133(2), pp.291-307.
Sonka, M., Hlavac, V. and Boyle, R., 2014. Image processing, analysis, and machine vision. Cengage Learning.
Zarnowiecki, D., Sinn, N., Petkov, J. and Dollman, J., 2012. Parental nutrition knowledge and attitudes as predictors of 5–6-year-old children’s healthy food knowledge. Public health nutrition, 15(7), pp.1284-1290.
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