What Is Embedded AI?
Embedded AI refers to running machine learning models directly on microcontrollers using lightweight frameworks such as TinyML.
A typical embedded AI system includes:
- Microcontrollers: ESP32, Arduino Nano 33 BLE, STM32
- Sensors: Accelerometer, vibration, temperature, microphone
- On-device AI: TinyML inference without cloud or internet
- Real-time decision-making
These features make embedded AI projects future-proof, industry-oriented, and technically strong for final year submissions.
Why Choose Embedded AI for Final Year Projects?
1. High Demand in Core + AI Jobs
Embedded AI skills are widely used in:
- Automotive electronics
- Predictive maintenance systems
- Smart IoT devices
- Healthcare and wearable electronics
- Consumer electronics
Companies increasingly prefer engineers who understand both embedded systems and AI.
2. Ideal for ECE / EEE / Electronics Students
Embedded AI projects:
- Align with core electronics syllabus
- Use microcontrollers and sensors
- Avoid heavy software or GPU dependency
- Fit AICTE and university-approved project categories
3. Strong Viva & Placement Value
Students can confidently explain:
- Sensor data acquisition
- Feature extraction
- TinyML model working
- On-device inference
- Real-time outputs
This significantly improves viva performance and placement interviews.
4. Low Hardware Cost (ESP32 + TinyML)
Compared to GPU-based AI projects:
- Runs on low-cost microcontrollers
- No cloud or internet required
- Easy to demonstrate in college labs
- Budget-friendly for students
How Source Code Is Presented in This Blog
For academic final year projects:
- Full code dumping is discouraged
- System understanding and design flow matter more
This blog follows standard academic practice by providing:
- Clear system architecture
- Module-wise core logic snippets
- Documentation-friendly structure
The snippets help students understand, customize, and implement the complete project independently.
Top Embedded AI Final Year Projects with source code
(With System Flow & Core Logic)
Project 1: TinyML-Based Machine Fault Detection Using ESP32
Project Description
This project detects machine faults by analyzing vibration data using a TinyML model deployed on ESP32. Such systems are widely used in industrial predictive maintenance.
System Design Flow
Machine Vibration
↓
MPU6050 Accelerometer
↓
ESP32 (Sensor Acquisition)
↓
Feature Normalization
↓
TinyML Model (Normal / Fault)
↓
LED / Buzzer Alert
Core Embedded AI Logic (ESP32)
(Assumes TinyML interpreter and tensors are already initialized)
#include
#include "MPU6050.h"
MPU6050 mpu;
int16_t ax, ay, az;
void readSensor() {
mpu.getAcceleration(&ax, &ay, &az);
}
void runInference() {
input->data.f[0] = ax / 16384.0;
input->data.f[1] = ay / 16384.0;
input->data.f[2] = az / 16384.0;
interpreter->Invoke();
if (output->data.f[1] > 0.7) {
digitalWrite(LED_BUILTIN, HIGH);
} else {
digitalWrite(LED_BUILTIN, LOW);
}
}
Academic Justification
- Real-time embedded AI implementation
- Low-cost hardware
- Excellent scope for documentation and viva
Project 2: Embedded AI Voice Command Control System (Offline)
Project Description
This project performs offline voice recognition using TinyML, without internet or cloud dependency.
System Design Flow
Voice Input
↓
Microphone
↓
ESP32 Audio Capture
↓
MFCC Feature Extraction
↓
TinyML Keyword Model
↓
Relay / Device Control
Core Logic
float features[40];
void loadFeatures() {
for (int i = 0; i < 40; i++) { input->data.f[i] = features[i];
}
}
void detectCommand() {
interpreter->Invoke();
if (output->data.f[1] > 0.8) {
digitalWrite(RELAY_PIN, HIGH);
} else {
digitalWrite(RELAY_PIN, LOW);
}
}
Academic Justification
- Fully offline AI system
- High interview relevance
- Suitable for ECE and EEE students
Project 3: Human Activity Recognition Using Embedded AI
Project Description
This project classifies activities such as walking, sitting, and running using TinyML on ESP32.
System Design Flow
Human Motion
↓
Accelerometer
↓
ESP32 Processing
↓
Feature Vector
↓
Embedded AI Model
↓
Activity Classification
Core Logic
void classifyActivity() {
interpreter->Invoke();
int activity = 0;
float maxVal = 0;
for (int i = 0; i < 3; i++) { if (output->data.f[i] > maxVal) {
maxVal = output->data.f[i];
activity = i;
}
}
}
Academic Justification
- Real-time AI execution
- Healthcare and wearable relevance
- Easy to demonstrate during evaluation
Why These Projects Are Accepted by Universities
These embedded AI projects satisfy Indian university final year requirements:
- Real-time hardware implementation
- Embedded systems + AI integration
- Low-cost components
- Clear objectives and results
- Strong documentation and analysis scope
Students can easily prepare:
- Block diagrams
- Flowcharts
- Algorithms
- Model training explanation
- Result analysis
Why Full Source Code Is Not Shared Publicly
Final year project evaluation focuses on:
- Understanding system design
- Correct working logic
- Real-time execution
- Ability to explain during viva
Universities discourage blindly copied code. Providing concept-level, modular code helps students:
- Build originality
- Improve technical confidence
- Customize projects as per evaluation norms
Academic Recommendation for Final Year Students
- Use this blog to understand project feasibility and flow
- Implement additional modules independently or with guidance
- Customize the project as per university or AICTE norms
- For complete implementation, students may seek guidance from college-approved mentors or embedded systems training centers.
