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Description:
EduBot is an AI educational robot that can provide personalized learning experiences using innovative features like emotion recognition, AR and adaptive learning. This is a set of capabilities adapted to run within the Sipeed Maixduino Kit hardware constraints.
Key Features And Implementation Sipeed Maixduino
1. Emotion Recognition:
Detects and responds to student emotions, optimizes learning processes in real time to keep students engaged and motivated.
Implementation:
Facial Emotion Detection:
Model: MobileNetV2 + FaceNet.
Processing speed: 5 ~ 10 FPS on Sipeed Maixduino (subject to operation optimization)
64-bit depth: Memory Usage with Inference around 2-3 MB out of the 8MB SRAM.
Solution: Conditional compilation and with a more-than-few neurons, scalable reduction in model sizes (~30% of the total memory
usage) would make it viable for real-time emotional recognition on mobile systems handling their regular multitasking.
2. Augmented Reality (AR): .
Introduces interactive 3D content into the learning environment, allowing students to explore topics such as history and science in a hands-on, interactive way.
Implementation:
Camera-Based Tracking:
Resolution: 60 FPS (QVGA) or 30 FPS ( VGA).
Speed: 30 FPS real-time tracking via onboard camera.
AR Content Display:
and of course… model size: Some simple 3D models will be around the disk size of 100-500 kb to render quickly.
RAM Usage: AR tracking and rendering require about 2 MB of static SRAM, Assets are loaded/unloaded dynamically.
Optimization: Use simplified 3D models (e.g., low-poly) and limit the number of active AR objects to 2-3 to ensure smooth
performance.
3. Voice Interaction:
EduBot offers advanced voice recognition similar to Alexa, allowing students to interact using natural language commands. They can ask questions, request information, and receive instant, voice-based responses, making EduBot an intuitive and hands-free educational assistant.
Implementation:
Processor Specifications:
Kendryte K210 Processor: The Sipeed Maixduino is equipped with a RISC-V Dual Core 64-bit processor, running at 400MHz. The processor
includes a dedicated neural network processor (KPU) for AI tasks like voice recognition, allowing for efficient real-
time processing of complex audio inputs.
Microphone Specifications:
External Microphone Array: The Maixduino supports a microphone array with multiple omnidirectional microphones, such as the SPH0645 or
INMP441, capable of capturing high-quality audio from various directions, which enhances the accuracy and
responsiveness of voice commands.
Audio Processing:
FFT Accelerator: The board features a Fast Fourier Transform (FFT) accelerator that breaks down audio signals into frequency
components, aiding in precise voice recognition and interpretation.
4. Adaptive Learning:
EduBot customizes lessons based on each student's pace and needs, providing personalized learning pathways for optimal progress.
Implementation:
Real-Time Adjustments:
Processing Speed: Adaptive algorithms can adjust lesson difficulty within ~200 ms based on current student performance.
Memory Usage: The adaptive learning module requires ~1-2 MB of SRAM, depending on the complexity of the algorithms.
Optimization: Cache frequently accessed student data in RAM, reducing read/write operations to the microSD card and improving
response times.
Real-Time Performance Analysis:
Processor: The Sipeed Maixduino uses the RISC-V Dual Core 64-bit Kendryte K210 processor with a neural network processor (KPU) to
analyze how a student is performing during lessons.
Adjusting Content on the Fly:
Machine Learning: The neural network processor allows EduBot to adjust the difficulty of questions or topics based on the student's current
understanding.
Creating a Personalized Learning Path:
AI Algorithms: The board's AI capabilities are used to create a unique learning profile for each student, which is constantly updated as
they learn.
5. Gamified Learning:
Functionality: EduBot integrates rewards and challenges into the learning process, making it fun and competitive. This approach encourages continuous engagement, motivating students to achieve learning goals while enjoying the experience.
Implementation:
FPS: Games can run at 30-60 FPS depending on complexity, with simple graphics and logic.
Optimization: Use sprite-based graphics and precomputed logic to minimize processing requirements and ensure smooth
gameplay.
Processor Specifications:
Kendryte K210 Processor: The Sipeed Maixduino’s RISC-V Dual Core 64-bit processor, clocked at 400MHz, handles real-time game logic,
tracking student progress and dynamically adjusting challenges to match their skill level.
Display Integration:
LCD Screen Support: Such as a 2.4-inch or 3.5-inch TFT display, to visually present challenges, scores, and rewards in an engaging
manner. The display can also show interactive elements that respond to the student’s actions during gameplay.
Interactive Elements:
RGB LED Strip and Buzzer: RGB LED strip to provide visual feedback,. Additionally, a buzzer can be used to give auditory feedback, such as
alerting students to new challenges or celebrating their achievements.
Why the Sipeed Maixduino?
-> EduBot Uses the Sipeed Maixduino The primary reasons for choosing this board was that it has both lots of AI processing grunt AND IoT
connectivity in a small, and relatively inexpensive form factor. Here is how its features meet EduBot requirements:
-> RISC-V Dual Core 64-bit Processor and KPU: for real-time image/speech/vision processing. For things like emotion recognition and AR, you
are looking at up to 60 FPS in QVGA resolution.
-> ESP32 Module: Provides reliable IOT connectivity and that enables EduBot to interact with other devices or get data from databases, cloud
services.
-> MEMS Microphone and Audio Capabilities: It allows the users to do real-time voice recognition along with audio-based emotion detection.
-> Available for Camera ModuleSupportAR and real-time emotion recognition tasks, with real-time processing capabilities.
-> MicroSD Card Storage: Offers ample storage for profiles, progress data, and multimedia content, supporting up to 16 GB of external storage.
## Conclusion
By effectively integrating the features of the Sipeed Maixduino Kit and optimizing resource usage, EduBot provides a powerful, flexible, and engaging learning experience. The implementation of efficient AI models, thoughtful memory management, and energy-saving methods guarantees that EduBot can excel in educational settings, offering tailored learning with immediate feedback.
## Final Note
We are incredibly curious and excited to work on this board, pushing the boundaries of what the Sipeed Maixduino Kit can achieve. This project will showcase our skills in AI, IoT, and educational technology, culminating in a complete, innovative product that has the potential to revolutionize learning environments. We look forward to bringing EduBot to life and demonstrating its capabilities in real-world applications.