Auto-formation group chat for fitness application with rule-based / Amar Aslam Ramli
People with an establish behavior were actively seeking ways to maintain and increase their fitness behavior which led many individuals need an advice on exercise from personal trainer. However, they actually do not know where to find the nearest trainer especially at the uncommon areas. Therefore,...
| Main Author: | |
|---|---|
| Format: | Student Project |
| Language: | English |
| Published: |
Faculty of Computer and Mathematical Sciences
2018
|
| Subjects: | |
| Online Access: | http://ir.uitm.edu.my/id/eprint/21346/ http://ir.uitm.edu.my/id/eprint/21346/1/TD_AMAR%20ASLAM%20RAMLI%20M%20CS%2018_5.pdf |
| Summary: | People with an establish behavior were actively seeking ways to maintain and increase their fitness behavior which led many individuals need an advice on exercise from personal trainer. However, they actually do not know where to find the nearest trainer especially at the uncommon areas. Therefore, by the aim of this project is to facilitate and solve the problem by developed FitwithTrainer android application with auto-formation group chat for clients and trainers along with rule-based expert system for classification of trainer in term of trainer’s levels and specialties in website system. Moreover, the algorithm used for rule-based expert system is forward chaining method. As this project’s aim to develop android mobile application system, there are three important modules such as developed GPS system which used Google Maps API as location based for detection of online users. Next, developed an auto-formation group chatting where to create online group chat for nearby users and last but not least create an instant messaging for private chatting between users. Thus, some phases of SDLC were used and the chosen phases such as requirement analysis, design, development, evaluating and documentation. This project are focuses on the FitwithTrainer android application with auto-formation group chatting as it is use geofencing technique in GPS to detect the nearest users. Then, for the appropriate testing for this project is the effectiveness testing which it has been done in order to test the system effectively for users and the major part are the accuracy of geofencing technique. Based on the testing result, it can be conclude that this project has successfully developed FitwithTrainer website and android mobile application system. For future recommendation works, this project can be extended further by applying the geolocation technique to detect the accurate location of the users in order to find nearest clients and trainers. Lastly, added some features such as include the registration in the android mobile application system as ease users to register instead of register in the website system. |
|---|