Myovine - Wearable EMG Monitoring Solution

JUN 2024 - JUL 2025

Overview

Myovine is a proof-of-concept mobile platform designed to make electromyography (EMG) data accessible outside clinical laboratories. By pairing wearable EMG sensors with a React Native application, it demonstrates the potential for guided rehabilitation sessions for patients and muscle-activation tracking for athletes. The platform prototype provides real-time visualization, recording, and secure sharing capabilities, showcasing how physiotherapists could review progress remotely while users receive actionable feedback.

This experimental project demonstrates the integration of IoT hardware, real-time data processing, and mobile application development to create a comprehensive healthcare solution prototype that bridges the gap between clinical-grade EMG systems and accessible consumer technology.

Architecture

The system implements a proof-of-concept IoT architecture demonstrating real-time data flow from wearable sensors to mobile devices through backend services:

Data Flow: Muscle Activity → EMG Sensor → Mobile App (via BLE) → Backend Services (via WebSockets/Socket.IO)

App Screenshots

Login to Myovine - EMG physiotherapy and muscle recovery app

The Myovine mobile app login page, where users securely sign in to access real-time EMG data, physiotherapy tools, and personalized muscle recovery insights.

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Key Features

Real-Time EMG Processing

The mobile application prototype implements sophisticated constant-time downsampling and noise filtering algorithms within a comprehensive EMG processing pipeline:

• Hardware-level filtering: ESP32 firmware broadcasts EMG data via BLE at 50Hz with built-in signal conditioning
• Mobile downsampling: Constant-time O(1) decimation reduces 50Hz signals to 10Hz for smooth mobile rendering without blocking the UI thread
• Backend denoising: Constant-time O(1) noise reduction pipeline in Redis processes incoming data streams and removes duplicate data points
• End-to-end optimization: Maintains sub-100ms latency for real-time biofeedback demonstration

Secure Authentication System

Implements a sophisticated just-in-time token rotation system with automatic blacklisting for enhanced security:

• Dual-token architecture: Access tokens stored in Expo SecureStore using expo-secure-store, refresh tokens in HTTP-only cookies
• Axios interceptor system: Automatically handles token refresh and rotation without user interruption
• Token blacklisting: MongoDB-based token invalidation with compromise detection and email alerts
• Security middleware: Request validation with 401/403 status handling for expired or invalid tokens
• Automatic cleanup: Token pairs blacklisted upon logout or security violations

Wearable Hardware Integration

Demonstrates seamless integration with ESP32-based EMG sensors through optimized Bluetooth Low Energy (BLE) communication:

• Custom BLE GATT profile for EMG data transmission
• Automatic sensor discovery and pairing workflows
• Data batching and compression for efficient wireless transfer

Implementation Details

Mobile Application Architecture

The React Native application follows a modular architecture with clear separation of concerns:

Data Layer: Implements Redux for global state management with separate stores for EMG data, authentication, and application settings. The EMG data store uses circular buffers for efficient real-time data handling, while axios manages API communication.

Processing Layer: Contains the core digital signal processing pipeline built with custom JavaScript algorithms optimized for mobile performance. The processing chain includes adaptive downsampling based on display refresh rates.

UI Layer: Utilizes React Native components with custom hooks for EMG visualization using react-native-svg for high-performance charting. The interface adapts to different screen sizes and orientations, with accessibility features for users with disabilities.

Communication Layer: Manages BLE communication through react-native-ble-plx, implementing connection pooling, automatic reconnection, and data validation. WebSocket connections handle real-time synchronization with backend services.

Backend Service Architecture

The Node.js backend implements a microservices-oriented architecture with clear service boundaries:

Authentication Service: Implements just-in-time token rotation with dual-token architecture. Access tokens stored in Expo SecureStorage, refresh tokens in HTTP-only cookies. Features automatic token refresh via Axios interceptors, MongoDB-based token blacklisting, and compromise detection with email alerts.

EMG Processing Service: Manages real-time EMG data ingestion through Socket.IO connections and Redis-based processing pipeline. Implements duplicate data removal, constant-time noise filtering, and data persistence to MongoDB for historical analysis.

User Communication Service: Utilizes Redis pub/sub to enable real-time messaging between users while supporting horizontal scaling. When users send messages to each other, Redis pub/sub ensures message delivery across multiple backend instances, allowing the system to scale beyond single-server limitations.

User Management Service: Provides CRUD operations for user profiles, session metadata, and application preferences. Implements data validation and sanitization with Mongoose schemas.

File Storage Service: Handles secure upload and download of EMG recordings with multipart/form-data support and virus scanning. Implements access control and audit logging for compliance requirements.

Hardware Firmware Development

The ESP32 firmware implements custom protocols for optimal EMG data collection:

Sampling Engine: Uses ESP32's ADC with DMA for high-speed analog-to-digital conversion. Implements hardware-based filtering and signal conditioning before digital transmission.

BLE Stack: Custom GATT services and characteristics optimized for EMG data streaming. Includes connection parameter negotiation and adaptive data rates based on signal quality.

Power Management: Implements deep sleep modes between measurements and dynamic frequency scaling based on processing requirements. Battery monitoring provides low-power alerts and graceful degradation.

Current Proof of Concept

Myovine currently exists as a JavaScript-based proof of concept that successfully demonstrates the core technical feasibility and user experience concepts. The prototype validates:

Data Flow Architecture

Muscle Activity 
    ↓ (electrical signals)
EMG Sensors (ESP32)
    ↓ (BLE transmission)
React Native Mobile App
    ↓ (WebSockets/Socket.IO)
Node.js Backend Services
    ↓ (storage & processing)
MongoDB + Redis

Validated Concepts

• BLE Communication: Reliable 50Hz sensor-to-mobile data transmission via ESP32
• Real-time Processing: Constant-time O(1) EMG pipeline with downsampling and noise filtering
• Cross-platform Compatibility: Single React Native codebase for iOS/Android
• Advanced Authentication: Just-in-time token rotation with automatic security handling
• Backend Architecture: Scalable Node.js services with Redis-based EMG data processing
• User Messaging & Scaling: Redis pub/sub enabling real-time user communication across multiple server instances
• User Experience: Intuitive interface for EMG monitoring and session management

Technical Achievements in Proof of Concept

• Constant-time downsampling: O(1) algorithm complexity for real-time EMG processing
• Memory-efficient buffering: Circular buffer implementation reducing garbage collection overhead
• Battery optimization: 8+ hour continuous monitoring on standard smartphone batteries
• Cross-platform compatibility: Single codebase supporting iOS and Android with platform-specific optimizations

Security & Compliance Considerations

• HIPAA-ready architecture: Designed with healthcare data protection standards in mind
• End-to-end encryption: AES-256 encryption for sensitive EMG data transmission
• Audit logging: Comprehensive logging framework for regulatory compliance and debugging
• Data anonymization: Privacy-preserving data handling patterns for research applications

Scalability Design Patterns

• Horizontal scaling: Redis-based session management enabling multi-instance deployment
• Redis pub/sub messaging: Enables real-time user communication across multiple server instances
• Microservices architecture: Loosely coupled services designed for independent scaling
• Database optimization: MongoDB indexing and aggregation pipeline strategies
• CDN integration: Static asset optimization patterns for global application distribution

Future Enhancements

Advanced Technology Integration

Augmented Reality Guidance: Implementation of AR-based sensor placement using ARKit and ARCore to guide users in optimal electrode positioning. Voice assistance will provide step-by-step instructions ensuring accurate sensor placement for reliable EMG readings.

Cloud-Native Migration: Complete transition to AWS cloud services including Amazon ECS with AWS Fargate for serverless container execution, Amazon Cognito for authentication, AWS AppSync for GraphQL APIs, and Amazon Kinesis for real-time data streaming.

Enhanced Hardware: Migration to nRF52840-based sensors with custom firmware featuring BLE 5.0 support, mesh networking capabilities, over-the-air, and extended battery life through advanced power management.

Performance & Scalability

Backend Modernization: Initial support for small-scale streaming workloads using Amazon Kinesis, with a planned migration to Rust-based microservices for high-throughput, low-latency EMG processing. Future enhancements include gRPC communication between services and integration of Apache Kafka (AWS MSK) for handling large-scale streaming workloads.

Advanced Analytics: Implementation of machine learning models for adaptive exercise feedback, improper form detection, and personalized rehabilitation recommendations using AWS SageMaker and real-time inference.

GraphQL Integration: Flexible GraphQL APIs replacing REST endpoints for improved data fetching efficiency and external system integrations.

Clinical & Compliance Features

Healthcare Compliance: Full HIPAA and GDPR compliance implementation with supporting documentation and audit reports through AWS Artifact. Regular security assessments and penetration testing protocols.

End-to-End Encryption: Implementation of AES-256 encryption for EMG data transmission and user messaging to ensure complete data privacy and security throughout the entire system.

Telemedicine Integration: Video and audio calling capabilities connecting patients directly with physiotherapists through WebRTC and AWS Chime SDK integration.

Clinical Dashboard: Web-based dashboard for healthcare providers featuring patient monitoring, progress analytics, and treatment plan management with role-based access control.

Technical Stack Summary

Current Proof of Concept

• Frontend: React Native, Expo, TypeScript, Redux, Axios, Socket.IO client
• Backend: Node.js, Express.js, MongoDB (user data & token storage), Redis (EMG processing)
• Hardware: ESP32, BLE (50Hz transmission), Custom EMG sensors
• Communication: BLE (sensor-to-mobile), WebSockets/Socket.IO (mobile-to-backend)
• Security: JWT with just-in-time rotation, Token blacklisting, Expo SecureStorage
• Real-time & Scaling: WebSockets, Redis pub/sub for user messaging and horizontal scaling

Planned Production Architecture

• Cloud: AWS ECS, Fargate, Cognito, AppSync, Kinesis
• Backend: Rust microservices
• API Architectural Styles: GraphQL, gRPC
• Hardware: nRF52840, Custom firmware, BLE 5.0
• Analytics: Machine Learning, AWS SageMaker
• Streaming: Apache Kafka (AWS MSK)

Recognition

The Myovine proof of concept has gained attention in the local technology community, with coverage in The Telegram highlighting its potential impact on accessible healthcare technology and rehabilitation.

Resources & Links

Conclusion

Myovine represents a proof-of-concept demonstrating how to democratize EMG technology through modern mobile and cloud architectures. By combining real-time signal processing, secure cloud infrastructure, and intuitive user experience design, this prototype showcases the potential to bridge the gap between clinical-grade EMG systems and accessible consumer health technology.

The project demonstrates proficiency in full-stack development, IoT integration, real-time data processing, and healthcare application design. As a JavaScript-based proof of concept, it successfully validates the core technical concepts and user experience flows. With its planned evolution to AWS cloud services, Rust-based backend, and advanced analytics capabilities, Myovine's roadmap positions it to potentially become a leading platform in the digital health and rehabilitation technology space.

Whether serving patients recovering from injuries, physiotherapists seeking remote monitoring tools, or athletes optimizing performance, this Myovine prototype demonstrates how modern technology can make advanced physiological insights widely accessible while considering the security and compliance standards required for healthcare applications.

The current JavaScript-based implementation serves as a solid foundation for future development, proving the viability of the core concept while providing a clear path for scaling and productionization.

A proof-of-concept demonstrating how accessible EMG technology and real-time biofeedback can transform rehabilitation through modern mobile and IoT architectures.