ESP32-S3 Edge AI Projects

Advanced machine learning inference on embedded hardware: hand gesture recognition and human activity classification using ESP-DL framework. Achieving real-time performance with minimal power consumption on resource-constrained devices.

Hand Gesture Recognition System

Hand Gesture Recognition

Advanced computer vision system leveraging custom CNN architecture to interpret hand gestures in real-time. Features optimized inference pipeline with 95% accuracy and sub-50ms latency, perfect for interactive IoT applications.

CNN ESP-DL OpenCV Real-time
Human Activity Recognition System

Human Activity Recognition

Intelligent activity classification system using deep learning to analyze IMU sensor patterns. Processes accelerometer time-series data to identify human activities with >90% accuracy on embedded hardware.

Deep Learning IMU Fusion Time Series ESP32-S3

Project Overview

These cutting-edge projects showcase the successful implementation of sophisticated AI models on ESP32-S3 microcontrollers using Espressif's ESP-DL framework. Both solutions achieve real-time machine learning inference on resource-constrained embedded devices without cloud dependency, demonstrating the transformative potential of edge AI in modern IoT applications.

The hand gesture recognition system represents a breakthrough in embedded computer vision, utilizing a custom-designed CNN architecture to interpret complex hand movements captured through an integrated camera module. Meanwhile, the human activity recognition system processes multi-axis IMU sensor data through advanced deep learning models to classify diverse human activities from accelerometer time-series patterns with remarkable precision.

Both projects required extensive optimization engineering including advanced model quantization techniques, sophisticated memory management strategies, and real-time performance tuning to achieve production-ready results on embedded hardware with strict power and computational constraints. The solutions demonstrate practical TinyML deployment at scale.

≤50ms
Inference Time
>90%
Accuracy
<2MB
Model Size
<100mA
Power Usage

My Key Contributions

1
Hand Gesture Recognition System

Designed and trained a CNN model for recognizing 10+ hand gestures with 95% accuracy, optimized through quantization for ESP32-S3 deployment with sub-50ms inference times.

2
Human Activity Recognition System

Developed deep learning models to classify 6+ activities from IMU time-series data, achieving >90% accuracy with real-time processing on resource-constrained hardware.

3
Model Optimization & Deployment

Applied quantization and pruning techniques to reduce model size by 75%, implementing efficient memory management and real-time inference pipelines using ESP-DL framework.

4
End-to-End System Integration

Integrated camera modules, IMU sensors, and display systems with optimized firmware, creating complete demonstration applications with intuitive user interfaces.

5
Performance Engineering

Achieved production-ready performance metrics: <50ms inference, <2MB model size, <100mA power consumption through systematic optimization and hardware-specific tuning.

Implementation & Technical Highlights

Development Workflow

  • Data collection and preprocessing using Python and TensorFlow for model training on powerful hardware.
  • Custom neural network architecture design optimized for edge deployment with minimal computational overhead.
  • Model quantization and pruning to reduce size by 75% while maintaining accuracy for embedded deployment.
  • ESP-DL framework integration with optimized memory allocation and real-time inference scheduling.
  • Multi-task FreeRTOS implementation ensuring deterministic latency for sensor data processing and UI responsiveness.

Technical Architecture

  • Efficient data pipeline with preprocessed sensor inputs feeding directly into optimized neural network inference.
  • PSRAM utilization for model parameters with SRAM optimization for intermediate activations and buffers.
  • Power-aware scheduling with sleep modes between inference cycles, minimizing overall power consumption.
  • Robust error handling and graceful degradation for production-ready embedded deployment.

These projects showcase the practical application of advanced machine learning techniques on embedded hardware, demonstrating that sophisticated AI capabilities can be deployed effectively in resource-constrained environments.

Technologies & Frameworks

Programming Languages

C C++ Python

Machine Learning & Embedded Frameworks

TensorFlow ESP-DL ESP-IDF FreeRTOS OpenCV NumPy

Hardware & Sensors

ESP32-S3 Camera Module IMU Sensors LCD Display PSRAM

Performance Metrics & Achievements

Key Performance Indicators

95%
Gesture Recognition Accuracy
>90%
Activity Classification Accuracy
≤50ms
Maximum Inference Time
75%
Model Size Reduction

Technical Optimizations

  • Quantization from 32-bit to 8-bit precision reducing memory footprint by 4x
  • Neural network pruning removing 60% of parameters while maintaining accuracy
  • Hardware-specific optimizations leveraging ESP32-S3 dual-core architecture
  • Power optimization achieving <100mA average consumption during active inference

Project Impact & Resources

Project Achievements

  • Successfully deployed production-ready AI models on resource-constrained ESP32-S3 hardware
  • Achieved 75% model size reduction while maintaining >90% accuracy through advanced optimization
  • Delivered real-time inference performance with <50ms latency suitable for interactive applications
  • Optimized power consumption to <100mA enabling battery-powered IoT deployments

Technical Documentation

These projects demonstrate the practical application of TinyML and edge AI principles, showcasing how sophisticated machine learning models can be effectively deployed on embedded systems without compromising performance or accuracy.

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