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Week 15 - System Integration ​

Assignment ​

  • Design and document the system integration for your final project

Idea Sketch ​

This idea initially started as a small in-car robot (Figure 1) capable of integrating biosensors. The robot collects data from the driver of an autonomous vehicle using patches and non-contact sensors, aiming to assess their fatigue and anger levels to ensure safety. Through iterations, we developed some more lightweight designs (Figure 2) for detecting stress and mood. A stress and mood detection device designed for autonomous vehicles, capable of uploading data to the cloud and providing feedback to the user. This device consists of the following six components:

  • Raspberry Pi 4B Data Processing Module:

    The Raspberry Pi 4B is used for data processing. It connects to an output screen to display processed results.

  • Seeed Studio XIAO ESP32S3:

    A microcontroller module that integrates various sensors and components for detecting stress and mood.

  • All-Solid-State Sodium-Selective Electrode:

    This electrode is used to measure sodium levels, which can help assess stress levels based on the presence of sodium in the saliva.

  • LED Lamp Beads: These are used to visually indicate the detected stress or mood levels by providing color-coded feedback.

  • Camera Module on the XIAO ESP32S3 Sense:

    A camera module integrated with the ESP32S3 Sense for detecting facial expressions or other visual cues to help assess the passenger's mood or stress.

  • Wi-Fi Connection:

    All components are connected through a Wi-Fi connection, allowing seamless data transfer between the processing unit and other modules.

Questions Answered ​

1.What does it do?

This is a stress and mood detection device designed for autonomous vehicles. It is installed on the car's air vent and uses biosensors (e.g., saliva sodium-ion detection) to monitor passengers or the driver for signs of stress and mood changes. Upon detection, it provides light and sound feedback, which has been shown to help alleviate symptoms associated with stress and negative moods.

2.Who’s done what beforehand?

Existing research has primarily focused on stress and mood detection in lab settings or general health data collection in daily life. However, few studies have adapted these methods for real-world autonomous driving environments with cloud-based analysis and real-time user feedback. Current solutions also tend to rely on bulky, inconvenient setups, making them impractical for seamless in-car integration.

3.What did you design?

Our design encompasses the following key components:

  • Biosensor data input & IoT integration – Saliva sodium-ion sensors paired with low-power Bluetooth/Wi-Fi modules for real-time physiological monitoring and vehicle system communication.

  • Circuit design & PCB fabrication – Custom input interface (signal conditioning for biosensors), main control board, and output system (multicolor LEDs + buzzer feedback).

  • Cloud processing & UI implementation – IoT platform for stress and mood pattern analysis, with dashboard visualization of historical nausea episodes and severity trends.

  • Enclosure design & 3D printing – Vent-mounted housing with ​​patch-style magnetic attachment​​ for secure docking to a matching base, featuring an integrated cavity to house all biosensors and concealed cable routing channels.

4.What sources did you use?

The following lab equipment and tools were utilized for prototyping:​​

  • ​​CNC Precision Cutter

  • ​3D Printer​

  • ​PCB Production​​

  • ​Sensor Calibration

5.What materials and components were used?

The project utilized the following materials and components:

  • ​Biosensing Module​​
    • Sodium-ion selective electrode for saliva analysis
  • ​Feedback System​​
    • RGB LED strip
    • 5V electromagnetic buzzer
  • ​​Control System​​
    • XIAO ESP32
    • Custom-designed PCB
  • ​Structural Components​​
    • PETG filament for 3D printed enclosure
    • 430 magnetic stainless steel plates

6.Where did they come from?

All components were sourced from reliable suppliers including electronics specialists like EDA and Seeed Studio for sensors and controllers, 3D printing materials from Bambu Studi. UNNC Fab Lab provided fabrication equipment like laser cutters and 3D printers.

7.How much did they cost?

The material and manufacturing costs are approximately 1,600 RMB.

What parts and systems were made? What processes were used? What questions were answered? What worked? What didn’t? How was it evaluated? What are the implications?