Wrist Detection for Medical Wearable Devices

Objective: Develop a deep learning solution that ensures wearable devices are correctly positioned on the wrist, which is critical for collecting accurate patient monitoring data.

Methodology:

• Data Collection: Gathered accelerometer data from real patients.
• Model Development: Designed and experimented with multiple deep learning architectures including CNN, RNN, LSTM, and GRU to capture the unique motion patterns of the wrist.
• Validation: Validated models using real-world patient data to guarantee robustness and reliability.

Solution: Created a real-time verification system that detects proper wrist placement. The system confirms correct device alignment before initiating continuous monitoring, ensuring that the collected vital signs are precise and actionable.

Impact:

• Enhanced Monitoring: By verifying device placement, the system improves the accuracy of vital sign measurements.
• Improved Patient Outcomes: Reliable data leads to better-informed clinical decisions and optimized patient care.
• Operational Efficiency: Reduces the likelihood of inaccurate readings, minimizing the need for manual rechecks and adjustments.

Technologies: Utilized TensorFlow and PyTorch for model training and development, Python and Pandas for data manipulation, and AWS for scalable deployment.

This innovative solution bridges the gap between wearable device data quality and clinical decision-making, ensuring that healthcare providers can depend on accurate patient monitoring every time.