Wrist detection for wrist-worn medical devices using DL

PROJECT INFOGRAPHIC

Accurate Placement Detection for Wearable Health Devices

PROJECT INFOGRAPHIC

Accurate Placement Detection for Wearable Health Devices using deep learning

Opportunity

The healthcare industry is increasingly relying on wearable medical devices to monitor patients’ vital signs accurately. However, ensuring that these devices are correctly worn on the wrist is crucial for reliable data collection. Misplacement can lead to inaccurate readings and compromised patient care. We saw an opportunity to enhance the reliability and effectiveness of these devices by developing a solution that can detect and confirm proper device placement using advanced deep learning algorithms using accelerometer data.

Approach

Our team developed a robust system that leverages accelerometer data to detect the activity of wearing the device on the wrist and taking it off.

Data Collection: We compiled an extensive dataset and meticulously annotated the data to train our classification model.
Feature Engineering: We extracted various features from our time series dataset and utilized feature ranking techniques to select the most significant ones.
Deep Learning Model Design and Training: We designed and trained multiple models, including CNN, RNN, LSTM, GRU, and hybrid models, and selected the best-performing one.
Validation and Optimization: We validated our model on real patient data and enhanced its accuracy through fine-tuning.

Solution

We developed a cutting-edge solution that integrates advanced deep learning techniques to ensure the accurate placement of wrist-worn medical devices. Our system analyzes accelerometer data in real-time, detecting motion patterns and vibrations to confirm whether a device is positioned on the wrist. This ensures reliable data collection and accurate monitoring of patients’ vital signs.

Accurate Placement Detection: Deep learning algorithms that effectively confirm whether device is worn or not.
Enhanced Data Reliability: Ensuring devices are correctly positioned and worn on wrist for reliable data collection.
Versatility: Our trained models are versatile and can function with data extracted from various hardware types. With some preprocessing, they are compatible with different wrist-worn devices, making them useful across a wide range of applications.

Impact

Our innovative solution has significantly improved the reliability and effectiveness of wrist-worn medical devices, leading to:

Accurate Placement Detection:Ensuring precise monitoring of patients’ vital signs with a support for multiple devices.
Improved Patient Monitoring: Contributing to better healthcare outcomes with more accurate vital sign monitoring.
Advanced Technology Integration: Demonstrating the potential of deep learning in healthcare applications.
Enhanced Patient Care: Improving the overall effectiveness and reliability of wrist-worn medical devices, leading to better patient care and monitoring.

Tools & Technologies

We utilized a range of advanced tools and technologies to develop this solution:

Deep Learning Frameworks: TensorFlow, PyTorch
Data Processing: Pandas, NumPy, Scikit-learn
Deep Learning Algorithms: CNN,RNN,LSTM, GRU
Development Environment: Python, Jupyter Notebooks, Visual Studio Code
Clout Platform: AWS

This project underscores our commitment to leveraging advanced technologies and rigorous research to enhance healthcare outcomes and deepen the understanding of complex medical conditions.