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Zarrar Ullah Khan

FAST · 2021 · I17 - 0406
Email
Phone
LinkedIn
GitHub

Academic

Program
CGPA
Year
2021
Education
Address
DOB

Career

Current role
Target role
Skills
Arduino, Proteus, Adafruit.io, IoT, NodeMCU ESP8266, Load Cell, RFID

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.
HitBOX (Martial Arts Strike Trainer) 
 
Technology advancement has infiltrated all types of fields in recent years and the realm of martial- 
arts is no different. Instead of old training methods passed down from generation to generation, 
athletes are becoming more inclined to use effective tech-based training tools which is starting to 
impact the standards of international martial arts competitions. For developing countries, to be 
able to compete with these athletes on the international stages, there is a need for a cheaper 
alternative to their smart training tools. 
HitBOX(Martial Arts Strike Trainer) is an IOT-based training tool designed for modern combat 
athletes which senses and records user strikes and displays relevant strike parameters in real-time 
and in forms of graphs for comparison and contrast.  
A single-point load cell along with an HX-711 load cell amplifier is used for this project. Data from 
the sensor is sent to the microcontroller, i.e NodeMCU ESP8266 to process Impact Force, Reaction 
Time, and Strike Velocity. These values are sent to the cloud and displayed on a user interface that 
is designed using a personalized dashboard on Adafruit.io.  
The entire system starts once a player is detected using an RFID module. The project provides a 
cost-effective, alternative training tool that presents reasonably accurate data. 
 
 
 
 
 
 
Technology Used: 
Arduino, Proteus, Adafruit.io 
 
Supervisor Name: 
 
 
Engr. Aamer Munir 
 
Group Members:   
Zarrar Ullah Khan (I17 - 0406)

AI enrichment

Zarrar Ullah Khan is a student who developed HitBOX, an IoT-based martial arts strike trainer using NodeMCU ESP8266 and load cell sensors. The project records impact force, reaction time, and velocity, displaying data via an Adafruit.io dashboard for cost-effective athletic training.
Skills (AI)
["IoT", "NodeMCU ESP8266", "Arduino", "Adafruit.io", "Proteus", "Load Cell Sensors", "RFID", "Data Visualization"]
Status: ai_done
Provenance
Source file: Graduate Directory FAST School of Engineering - 2022 Final Version (07-06-2022).pdf
From job #22 page 60
Created: 1778141844