EDL Project

Project Name: P-34 MEMS inertial sensor applications

Team Number: MON-05

Team Members:

Problem Statement and Solution:

Problem Statement:

Shock events can cause significant damage to equipment, goods, and infrastructure, leading to financial losses and safety and system integrity concerns. There is a need for a compact, reliable, and affordable system that can accurately detect and analyze shock events in real-time across various domains, including transportation, industrial machinery, and consumer electronics.

The project aims to develop a MEMS-based inertial sensor system that can detect and analyze shock events. The system will utilize a MEMS accelerometer to measure acceleration and a 6-DOF IMU to measure orientation data, a microcontroller to process the data and a bluetooth module to convey this information, wirelessly, in a concise format.

Solution:

The proposed system is designed to detect and analyze shock events in real-time using a combination of hardware and software components. Below is a detailed breakdown of the solution:

1. Hardware Components:
   • MEMS Accelerometer: A high-precision MEMS accelerometer will measure acceleration in three axes (X, Y, Z). This data will help identify sudden changes in motion indicative of shock events.
   • 6-DOF IMU (Inertial Measurement Unit): The IMU will provide orientation data, including angular velocity and linear acceleration, enabling a comprehensive analysis of the shock event's effects on the orientation of the product.
   • Microcontroller: A low-power microcontroller, STM32L552VET6Q will process the raw data from the sensors and store it for analysis. It will also store the summary of the shock events in its flash memory for instantaneous access.
   • Bluetooth Module: A Bluetooth module, HC-05, will wirelessly transmit the processed data to a paired device, such as a smartphone for further analysis or visualization.
   • Power Supply: The system will be powered by a rechargeable lithium-ion battery, ensuring portability and ease of use. The battery can be charged using an onboard charging circuit.
   • Storage: The system will include a microSD card module for additional data storage, allowing for long-term data logging and analysis. The microcontroller will interface with the microSD card to read and write data as needed.

Hardware Connections:

┌────────────────┐      SPI      ┌─────────────┐
│  Accelerometer ├──────────────►│             │
└────────────────┘               │             │
                                 │             │
┌────────────────┐      SPI      │             │
│      IMU       ├──────────────►│    STM32    │      UART      ┌────────────┐
└────────────────┘               │     MCU     ├───────────────►│  Bluetooth │
                                 │             │                │   Module   │
┌────────────────┐      SPI      │             │                └────────────┘
│    MicroSD     ├──────────────►│             │
└────────────────┘               └─────────────┘
                                       ▲
┌────────────────┐                     │
│ Li-ion Battery ├─────────────────────┘
└────────────────┘       Power

2. Software Components:
   • Sensor Data Acquisition: The microcontroller will interface with the MEMS accelerometer and IMU using SPI to collect real-time data.
   • Shock Detection: Interrupts on the IMUs detect shock events which involve threshold-based detection to toggle between sleep state and active state for power optimization.
   • Data Packaging and Transmission: The processed data will be formatted into a concise packet and transmitted via Bluetooth whenever the package is queried.
   • Data Visualization: A mobile application will be developed to visualize the data received from the microcontroller. The app will display real-time graphs of acceleration and orientation data, allowing users to analyze shock events easily.
   • Data Logging: The system will log data to the microSD card for long-term storage and analysis. The data can be retrieved later for further analysis or reporting.
   • Data Analysis: A Python script was developed to analyze the logged data, providing insights into the magnitude and duration of shock events.

Repository Structure

root/
├── 3d_models/                                    # Contains CAD models
    ├── Component_1/                                    # Files for CAD of outer casing
    ├── Component_2/                                    # Files for CAD of top half of inner casing
    ├── Component_3/                                    # Files for CAD of bottom half of inner casing
    └── README.md
├── others/                                       # Microcontroller firmware
    ├── Exploded View of the Assembly.png               # Contains the exploded view of the assembly
    ├── Assembled PCB.png                               # Contains the front and back view of the assembled PCB
    ├── PCB_Connection.png                              # Contains the connection diagram of the PCB
    ├── README.md                                       # Documentation for others/
    └── User Manual.pdf                                 # Contains the user manual for the PCB
├── pcb/PCB1/                                     # Schematic and layout files
    ├── design_files/                                   # Contains the schematic and layout files
    ├── footprints/                                     # Contains the custom footprint libraries created for this PCB
    ├── gerber_files/                                   # Contains the gerber files of the PCB layout
    ├── images/                                         # Contains images of the PCB layout and printed PCB
    ├── step_files/                                     # Contains the custom 3D models created for this PCB
    ├── symbols/                                        # Contains the custom symbols created for this product
    └── README.md                                       # Documentation for PCB1
├── reports/                                      # Milestone reports
    ├── Milestone_0.pdf
    ├── Milestone_1.pdf
    ├── Milestone_2.pdf

3. Future Enhancements:
   • Detailed Analysis: The system can be enhanced to include advanced algorithms for shock event classification, such as machine learning techniques to identify specific types of shocks or impacts.
   • User Feedback Integration: Implementing a feedback mechanism to gather user experiences and improve the system based on real-world usage.
   • Real-time Monitoring: Incorporating real-time monitoring features to provide immediate alerts and notifications during shock events.

This solution combines compact hardware, efficient algorithms, and wireless communication to deliver a reliable and affordable shock detection system suitable for various domains.

Project Experience

✅ Positive Takeaways

• End-to-End System Design: This project gave us practical experience building a complete embedded system—from sensor interfacing and PCB design to data processing and wireless communication. We learned how to integrate and coordinate multiple hardware and software components to achieve a real-time shock detection solution.

• Sensor Interfacing and Data Handling: Interfacing the MEMS accelerometer and 6-DOF IMU over SPI and handling data processing on the STM32L552VET6Q microcontroller helped us gain a deeper understanding of embedded communication protocols, interrupt-driven programming, and power optimization techniques.

• Cross-Platform Development: Developing a Bluetooth-based mobile application alongside microcontroller firmware and Python-based data analysis scripts introduced us to multi-platform integration and emphasized the importance of consistent data formatting and transmission protocols.

• Collaboration and Documentation: Working in a team of five, we coordinated tasks across hardware, firmware, software, and documentation. Milestone reports, CAD and PCB designs, and structured repository organization helped streamline our workflow and made version control manageable.

• Debugging and Testing: Real-world debugging of hardware-software interactions improved our troubleshooting skills. Each integration phase provided insights into performance bottlenecks, data inconsistencies, and how to systematically isolate issues.

⚠️ Challenges and Lessons Learnt

• Firmware Complexity: Coordinating SPI communication with multiple peripherals, managing memory, and handling Bluetooth data transmission was a bit complex. There were some issues with the clock supply to different communication protocols which were fixed eventually.

• Bluetooth Compatibility & Hardware Access: One of the major challenges we faced was the unavailability of the development board during critical stages of the project. This prevented us from flashing and testing the firmware on the actual hardware, which in turn delayed debugging and integration of Bluetooth functionality. As a result, we had to replace the original IC with a less efficient HC-05 module.

• PCB Design Revisions: There were a few minor hiccups in the 1st iteration of the PCB. The BOOT0 pin was not pulled down to ground and the silkscreen was wrong in two places (these details are mentioned in the PCB README). This taught us the importance of performing thorough design rule checks (DRC) and functional reviews before fabrication. We also learned to cross-verify microcontroller pin configurations and documentation to catch critical issues early in the design cycle.

• Time and Resource Management: Despite planning, some stages—especially integration and testing—took longer than expected. We learned the importance of scheduling buffer time for unexpected hardware or debugging delays.