1 Data Collection
Before performing experiments and results, it is important to design the experiment procedure and also analysis the Results after experiments. Before starting that our first task was to collect the data which we were going to use for experiments. Initially, we found the different sources for data collection. In section 3.1.1 we identified the sources which existed for data collection. Then we selected the most appropriate data source and started working on it section 3.1.2. Put all the data on a table for the record. In section 3.1.3 created a file and put all the data in that file and save in computer.
Before start any experiment I had to research about the problems, design experiments and select sources from where I can collect data. I chose 3 different categories of people e.g., common people of every age, young students, and professionals.
Ø Public places(mall, parks)
Ø College or university
I made a table below to record the data sources, main organizations where I can collect the data. What type of data I collected, format, and fee and if it was appropriate for my research.
After creating and completing that table my next step is to collect data and put that in another table. This table contains raw data, whatever I got from people. I recorded a number of data, where I saved it. It was for my knowledge purpose so that I would not lose or forgot it.
Next task is designing and implementation. Tasks divided into sub-tasks. In 1.2.1 we did pre-processing. 1.2.2 is based on Feature selection or dimension reduction. 1.2.3 is design phase. 1.2.4 is implementation.
Data pre-processing is very important as not all the people participate in the survey and the ones who participate will not give you all the answers to the questions. For evaluation, it is important to read the raw data, filter the duplicity or void data, resample it and generate new data record it in a new file. As shown in figure2.1.1 below.
After pre-processing, the next step is to select features from the results and reduce the numbers of random data under consideration. I made a new table to shows the pre-processing and reduction of random data and create a new file for my survey evaluation.
Experiments were based on a methodology which I purposed for my research. I selected hybrid methodology which is a combination of Qualitative and Quantitative approach. I collected data, analyzed it,searched health apps from Google play store and i-tune store, compare the apps, evaluate them and based on them made a questionnaire. I add 5 Questions in the questionnaire, which are the main questions are my survey. (figure2)
Before that survey, I designed some general features which were categorized by gender, age, education, Background. These features made it easy to got the rough idea about people and their preferences. And it's become easy to reduce the dimensions and feature selection.
As per today’s trend, most of the people have smartphones and they use it more multiple purposes. In today’s young generation is aware of mobile health applications and many of them use them as well. There are thousands of health-related apps. They all are based on different sections of health like; some focus on exercises, some on only Diet or water intake, running or sleep behaviors. Some have multiple activities. So at least one-third of the users uses and satisfied with these apps. But in spite of young people, other people like, housewives or elders are not much interested in these apps or not aware of them.
I did survey with different range of people categorized them in different sections, as they are student or retired or they are doing jobs or their own business. There was age range from 18 to over 45. Gender difference between male or female. Their education backgrounds. As per my survey, out of 100 27% have smart phones, 25% know about health apps, 17% download those apps and approximately same percentage of people use those apps. 9% people felt these apps are effective but only 5% are satisfied with these apps.
1 Experiment and Result Analysis
1.1.2 A collection of Data
1.1.3 Store the Data
1.2.1 Data pre-processing
1.2.2 Feature selection or data reduction
1.2.3 Experiment Design
220.127.116.11 Detailed Design Steps
18.104.22.168 Experiment Description
22.214.171.124 Software and Tools Used
126.96.36.199 Experiment Results
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