AI Engineer | Biomedical AI Specialist | Full-Stack Developer

Transforming healthcare and interactive systems through innovation in AI, and digital technology.

Not just a data scientist or a full-stack developer; I'm a Generalist passionate about all things tech (such as technology and techno music)! With a unique blend of science, technology, and a knack for innovation, I’m focused on building cutting-edge digital systems for real-world impact.

• Innovating in Digital Health: From smart insoles for gait analysis to real-time AI models for posture.
• Empowering Machines: Teaching them to recognize human movement and make insightful recommendations.
• Delivering on Execution: Building and deploying practical tech solutions for user-facing platforms.

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Project: Real-Time Pose Detection Platform

Company: Neurabody AI South Korea

Objective: Capture and analyze human movement in real time for applications in fitness and health.

Tech Stack: AWS Lambda, MediaPipe, WebRTC, Node.js

📋 The Challenge: Building a platform that runs real-time pose detection at low latency and high efficiency. It’s all about keeping performance smooth, minimizing delays, and achieving a seamless user experience across devices.

🔎 Current Focus: Optimizing memory and tackling memory “jank” on different browsers to deliver consistent quality.

Project: Bio-signal Processing & Sleep Analytics

Company: Wethm LLC. South Korea

Objective: Build a backend infrastructure capable of processing millions of data points to deliver precise, actionable sleep insights to users.

Tech Stack: AWS (DynamoDB, Lambda), Next.js, Node.js, Python, Docker, AI Modeling

📋 Challenges:

• Designing a high-throughput data pipeline to efficiently process over 2.5 million bio-signal data points per hour, enabling real-time sleep detection.
• Integrating advanced AI models to enhance bio-signal analysis and ensure accurate sleep pattern recognition.
• Developing scalable, optimized data models within DynamoDB to manage the vast amounts of incoming data seamlessly.
• Implementing premium features, including personalized sleep insights and recommendations, as part of a subscription model, aimed at providing tailored guidance based on individual bio-signals.
• Ensuring a resilient AWS infrastructure to handle high data loads while maintaining consistent performance and uptime.

🎯 Impact: The Sleep Intelligence Hub connects thousands of devices, enabling data-driven insights that help users improve sleep quality. This platform supports Wethm’s premium users by offering personalized feedback, empowering better sleep through actionable analytics.

Project: IoT-Powered Patient Activity Monitoring

Company: FlexoSense Pte. Ltd. Singapore

Objective: Develop a secure and accurate system for monitoring patient activity remotely, enhancing the quality of care and supporting lifestyle insights for healthcare providers.

Tech Stack: IoT Sensors, Android, Java, Python, Data Privacy & Security Frameworks, Machine Learning Models

📋 Challenges:

• Designing a data model with robust security, meeting stringent data privacy standards to protect sensitive patient information.
• Creating an activity detection model that achieves high accuracy (96%) across diverse patient profiles, providing clinicians with reliable insights into daily patient activity.
• Building an intuitive Android dashboard that enables hospitals to track patient metrics in real time, supporting remote monitoring and reducing the need for in-person follow-ups.
• Ensuring seamless integration within the MOH-funded pilot framework, collaborating closely with hospital teams for feedback and alignment.

🎯 Impact: This solution empowers healthcare providers to monitor patient health remotely with confidence. Now deployed in Singapore’s major hospitals, the system aids in delivering timely insights to clinicians, helping drive improved patient outcomes and lifestyle guidance.

Project: IoT-Powered Patient Activity Monitoring

Company: FlexoSense Pte. Ltd. Singapore

Objective: Deploy an AI-driven system that detects safety incidents with high accuracy, supporting real-time monitoring to enhance workplace safety and response times.

Tech Stack: IoT Sensors, Android, Java, Python, Machine Learning Models, Real-Time Data Processing

📋 Challenges:

• Developing an AI model capable of detecting safety incidents with a high degree of accuracy (98%) across diverse real-world scenarios and conditions.
• Conducting extensive on-site testing to refine model performance and ensure reliability in real-time applications, minimizing false positives.
• Implementing an intuitive Android dashboard that allows safety teams to monitor incident alerts in real time, providing quick insights and supporting prompt action.
• Ensuring the system integrates smoothly with existing safety protocols, aiding teams in incident management and improving overall workplace safety.

🎯 Impact: The system enhances incident detection capabilities, providing high-accuracy insights that are already helping clients monitor workplace safety in real time. By enabling rapid response to safety incidents, it plays a critical role in reducing workplace hazards and improving occupational health.

Language	Technology	Framework	Tools

🌩️ Cloud and Deployment

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