CogniSync

Inspiration

We are faced by a stark reality: 1 in 10 adults aged 45 and older experience worsening memory loss or confusion - early signs that often go unnoticed. What's most concerning is that these subtle changes can appear up to 10 years before becoming obvious symptoms. This gap between early signs and diagnosis is what inspired us to create CogniSync.

Core Technology

We developed a wearable device that serves as the foundation of our system. Using an integrated IMU sensor, it captures two critical types of data that provide early insights into cognitive health.

Movement Analysis

The device continuously tracks movement patterns through its integrated sensors. By processing acceleration, gyroscope, and magnetometer readings, we calculate stability indices from acceleration variance and measure movement efficiency through jerk analysis. These measurements allow us to identify early concerns in tremor patterns, gait stability, and fall risk assessment, providing early warnings of potential cognitive decline.

Voice Analysis

The same wearable device captures speech patterns, which research shows can be one of the earliest indicators of cognitive changes. Our analysis examines voice stability, where we look for readings above 65% for normal function. We also track attention patterns through speech rhythm, aiming for scores above 80%, and monitor memory indicators through processing speed, targeting above 90%. Key metrics like mean pause duration are measured against clinical thresholds, with 1.2 seconds being a crucial marker, while beta power measurements are compared to a 5.465 threshold for memory function assessment.

Cognitive Assessment

To complement the wearable, we developed engaging cognitive games that transform traditional assessments into interactive experiences. Our suite includes memory recall exercises that dynamically adjust memorization times, reaction-based challenges that introduce random variations, and pattern recognition tasks that scale in complexity. These games continuously adapt to player performance, ensuring both engagement and accurate assessment. By combining game performance with our wearable data, we aim to create a comprehensive picture of cognitive health that can detect subtle changes before they become apparent symptoms. The results from all three components - movement, voice, and cognitive games - are displayed through a dashboard that transforms these metrics into clear, actionable insights, enabling earlier intervention when it matters most. 

How we Built It

We integrated our wearable device's data streaming through TCP/IP sockets with a Python backend. For data analysis, we used NumPy for statistical computations of movement metrics like stability indices and jerk calculations. The voice analysis pipeline utilized pre-trained transformer models from Hugging Face for pattern recognition and emotion classification. We built our interactive dashboard using Streamlit and Plotly, creating real-time visualizations of sensor data and health metrics. The system stores user interaction data and cognitive game results in JSON format for persistent tracking and analysis.

Challenges We Ran Into

Our biggest challenges came from processing multiple data streams simultaneously and extracting meaningful data from voice patterns. Managing continuous sensor input while running voice analysis models required significant performance optimization. We also dealt with calibrating accurate health metric thresholds, especially aligning our voice stability and movement analysis with clinical standards. In overcoming these challenges, we were able to transform raw sensor and voice data into meaningful health insights through our monitoring platform.

Accomplishments that we're proud of

Our biggest achievement was successfully integrating complex health monitoring into a single, intuitive interface that makes data accessible and meaningful. We're also particularly proud of our selection of cognitive games, intended to balance assessment and engagement.

What we learned

The development process taught us crucial lessons about balancing real-time data processing with system performance, while maintaining accuracy. We discovered the importance of translating complex clinical metrics into user-friendly insights that anyone can understand. Most importantly, we learned how continuous sensor data, when properly analyzed, can reveal subtle patterns that indicate significant health trends.

What's next for CogniSync

We plan to expand our system by incorporating more sophisticated health metrics, developing additional cognitive games, and refining our threshold detection algorithms based on clinical research. We're also exploring ways to make our platform more accessible through mobile development and improved data visualization techniques.

Built With

• html
• imu
• machine-learning
• numpy
• plotly
• python
• streamlit
• tcp/ip
• transformers