Month: February 2026

BTE1522 DRE 2213 – Project Submission

Well done everyone!

Students from BTE 1522 and DRE2213 presented their final projects, and the outcomes were impressive =).

What began at the start of the course as an introduction to beginner Python programming through a simple Pygame slider game (Pygame assignments) has now evolved into fully functional sensor-based systems using Raspberry Pi and the BME280 environmental sensor.

This transition from a purely digital game environment to a real-world, physically embedded system, was intentional =).

By first grounding students in Python fundamentals (variables, loops, conditionals, event handling, and logic flow) through game development, students were able to focus later on how their code interacts with the physical world.

BTE1522 – IMU Data Collection

Learning Python Through Motion, Data, and Innovation: BTE1522 Project Showcase

Students from BTE1522 – Innovation (Python) recently presented their final projects, and the results clearly demonstrated how hands-on, sensor-driven learning can elevate Python programming skills.

In this course, students worked with the MPU6050 motion sensor on the LilEx 5 platform, moving beyond basic scripting to build end-to-end data-driven systems involving sensing, storage, and visualization.

Project Focus

Each student group was tasked to:

      1. Read motion data from the MPU6050 using Python

      2. Design and conduct structured data collection for different human movements

      3. Store the data in a database of their choice

      4. Build a dashboard to visualize and interpret the collected data

This workflow mirrors real-world IoT and data engineering pipelines.

Movement-Based Data Collection

Students collected sensor data based on well-defined criteria, including:

      1. Standing

      2. Leaning left and right (roll)

      3. Bending forward and backward (pitch)

      4. Lying down

They carefully controlled parameters such as:

      1. Sampling rate

      2. Timeframe per movement

      3. Sensor placement

      4. Calibration procedures

This encouraged students to think critically about data quality, consistency, and repeatability, not just code correctness.

From Raw Sensor Data to Insight

Using Python, students transformed raw accelerometer and gyroscope readings into structured datasets. They then explored different tools and platforms to:

      1. Build databases

      2. Create dashboards for visualization and interpretation

Through this process, students learned that innovation is not only about building something new, but also about making data understandable and useful.

Physical Embodiment as a Learning Strategy

Similar to DRE2213, this course emphasized learning through physical embodiment. Students could directly observe how body movement affected sensor readings, reinforcing their understanding of:

        1. Coordinate axes

        2. Sensor fusion concepts

        3. Time-series data behavior

By linking physical motion to Python code and visual dashboards, abstract programming concepts became concrete and intuitive.

Overall, student performance was very satisfying. Good job everyone.

The projects demonstrated strong engagement, creativity, and a growing confidence in Python programming.

The project videos embedded below highlight how students applied Python not just as a programming language, but as a tool for sensing, data analysis, and innovation.

DRE2213 – BM280 Data Monitoring – SULAM

Project Highlights

In their final projects, DRE2213 students successfully demonstrated:

  1. Closed-loop sensing systems
    Integrating the BME280 sensor with Raspberry Pi using Python, where sensor readings triggered real-time responses such as LEDs and buzzers.

  2. Data logging and storage
    Students independently explored multiple database solutions:

      • Firebase

      • Google Sheets / Spreadsheet-based logging
        This showed strong initiative and adaptability beyond what was explicitly taught.

  3. Dashboard development and visualization
    A wide range of dashboard approaches were implemented, including:

      • HTML-based dashboards

      • Adafruit IO

      • Flask web applications

      • Streamlit dashboards

Each solution reflected different design choices, yet all achieved the same goal: making sensor data meaningful, visible, and interactive.

https://youtube.com/shorts/KYIy1CRj6Nk?si=2cHH3TGO1eFjDp9y

https://www.youtube.com/shorts/KYIy1CRj6Nk

https://youtube.com/shorts/KYIy1CRj6Nk?si=2cHH3TGO1eFjDp9y

 

 

Learning Through Physical Embodiment

What stood out most was how BTE1522 and DRE2213 students connected abstract Python code to tangible outcomes. Seeing a buzzer activate, an LED respond, or a dashboard update in real time helped students understand what their code is doing, not just whether it runs.

This combination of:

      1. Digital embodiment (game-based learning with Pygame), and

      2. Physical embodiment (real sensors, real data, real feedback)

proved to be a powerful approach in helping students grasp programming concepts more deeply and confidently.

Reflection

The quality of the projects and the variety of technical approaches exceeded expectations. Students demonstrated not only programming skills, but also problem-solving, system integration, and creativity.

The embedded project videos below showcase their work and reflect a learning journey that truly bridges Python programming and real-world applications.

 

Nurul Hazlina – Feb 4th