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Bushra Fatima

FAST · 2022 · I18 - 0566
Email
Phone
LinkedIn
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Academic

Program
CGPA
Year
2022
Education
Address
DOB

Career

Current role
Target role
Skills
C, Python, Flask, PyCharm, Heroku, Java, Android Studio, Firebase, Tizen Studio

Verbatim text

The exact text the LLM saw on the page (or the booklet text from the old import). This is what powers semantic search.
SMARTSalah 

SMARTSalah will perform the task of activity recognition by using smartwatch sensors. The watch is 
tied to the wrist, and the sensors will record the tri-axial coordinates of the person performing 
Salah. The basic steps that will be recorded are Long-Standing (Qayam), Bowing (Raku), Short 
Standing (Qouma), 1st Prostration (Sajda-I), Short Sitting (Jalsa), 2nd Prostration (Sajda-II), Long 
Sitting (Tashahud).  The recording of all steps of the prayer gives us a pattern of Salah performed 
through which our system will tell whether the performed steps and Salah are correct and complete or not. The correctness of Salah depends upon the performance of each posture and the sequencing of postures which tells that performed Rakah is correct. If all the rakah including postures are correct and complete, this means that Salah is complete. The AI based mobile application will show the Salah profile to the person which contains all the statistics of the performed Salah.  
Features include:  
Average time spent on each posture of Salah i-e Qayam, Ruku, Sajda, Jalsa, and Tashahud. Time spent on each Rakah of performed Salah.Tells the user about any missed rakah or any extra prayed rakah. Tells the performer if he has missed any posture within the rakah and similarly if he performed any extra posture. Identify the missed Salah of the performer on daily, weekly, and monthly bases. SMARTSalah maintains the count of Farz and Sunnah in a day also and enlightens the user about how much sunnah he/she is offering alongside Farz Salah. The application also creates the provision of routine day supplications for the performer to recite while using this app.    
 
 
Technology Used: 
C, Python, Flask, PyCharm, Heroku, Java  
4.6, Android Studio, Firebase Tizen Studio 
Supervisor Name: 
Dr. Amna Basharat 
Co-Supervisor Name: 
Dr. Asma Ahmed  
Group Members:   
Mahnoor Raza (I18 - 0571) 
Bushra Fatima (I18 - 0566) 
Maha Riaz (I18 - 1652)

AI enrichment

Bushra Fatima is a student who contributed to a group project developing an AI-based mobile application for Islamic prayer activity recognition using smartwatch sensors. The project involved processing tri-axial sensor data to validate prayer postures and providing user statistics via a Flask and Android-based system.
Skills (AI)
["Python", "C", "Java", "Flask", "Android Studio", "Firebase", "Tizen Studio", "Activity Recognition", "Sensor Data Processing"]
Status: ai_done
Provenance
Source file: Graduate Directory FAST School of Computing 2022 Final Version (07-06-2022).pdf
From job #25 page 261
Created: 1778149999