Week 2 - Ethically Speaking: No Robots Will Be Harmed in the Making of This Project
- muhammadsirajbilal
- Feb 9
- 2 min read
Updated: Apr 14
This week's session focused on research ethics, which is an important element of our final-year projects. During the session, Dr. Fehmida and Mr. Roshan emphasised the necessity of upholding strong ethical standards, particularly in projects involving human participants or external data collection. The seminar highlighted the numerous ethics forms we must complete to guarantee that our projects can move forward responsibly and without causing harm to anyone involved.
For my project, iCue, I worked on completing the ethics screening and application forms, as I will require participants to test the device. The project is ethical as it does not require the personal information of the participants and does not harm anyone in any way. In addition, I modified key documents such as the Participant Information Sheet, Consent Form, and Debriefing Form according to my project requirements. These documents will be distributed to all participants to ensure that they are fully informed and that their participation is purely voluntary. These resources helped me grasp the responsibility that comes with conducting research with real users, and I now feel more confident in ensuring my project is ethical.
Breaking Down iCue’s Technical Implementation
While iCue may seem simple on the surface, the technical implementation requires a well-structured approach that integrates hardware, software, and AI models.
Hardware Setup: Sensors & Data Collection
The ESP32 microcontroller is the core of iCue, processing movement data captured by the MPU-6050 sensor attached to the cue stick. The MPU-6050 will collect the following data: Acceleration (to detect force and speed of the shot), Gyroscope values (to track angle and stability of the stroke), Motion Spikes (to determine when a shot is taken). Once collected, the ESP32 will transmit this data via Bluetooth to the mobile app for further analysis.
Software & Data Flow: Processing & Storing the Information
The software application will handle: Shot Detection & Data Filtering – Only capturing data when a shot is played, Cloud Storage (Firebase) – Storing all shot data for later ML model training, User Dashboard – Displaying shot history, accuracy trends, and training insights.
AI-Powered Analysis: Machine Learning Model
Once the shot data is collected, the Machine Learning Model will: Compare a player’s shot to a professional’s, Provide feedback on shot straightness and stability, Classify the player’s skill level (Beginner, Intermediate, Advanced), Recommend practice drills to improve accuracy.
Work Breakdown Structure (WBS)
To effectively manage my project, I worked on my Work Breakdown Structure (WBS) this week. This will help me divide the project into manageable phases, ensuring I stay on track. Below is the WBS:
I also made a Gantt Chart to ensure that I deliver the project on time.
Reflection
This week was not an easy week for me. While I was able to do the lab activities properly and complete my Work Breakdown Structure, I was not able to research much on the project and start with the formal project proposal. With my master's application entrance exam and the unfortunate passing away of my late grandfather (may his soul rest in peace), the project's progress came to a halt. Hopefully, I am able to bounce back and get back on track as soon as possible.
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