Fall Detection System

Project Context

This project was undertaken as part of my internship at Airfi, a technology-focused company working closely with hospitals, nursing homes, and other healthcare providers. The main goal was to develop a reliable and efficient fall detection system to enhance safety for elderly individuals. The system was designed to work in real-time using wearable devices equipped with accelerometers and gyroscopes.

Airfi specializes in creating innovative solutions that integrate seamlessly into existing healthcare infrastructures. This project was part of a larger initiative to leverage IoT devices to provide continuous monitoring for patients in residential care facilities. The system aimed not only to detect falls but also to collect valuable data that could be used to prevent future incidents by analyzing patterns of movement and activity.

Challenges

• Ensuring high accuracy in detecting falls while minimizing false positives. False alarms could lead to unnecessary stress for caregivers and healthcare providers.
• Managing noisy and incomplete data from the wearables during real-world usage. The data collected in different environments, such as hospitals and nursing homes, often contained artifacts that needed to be filtered out.
• Optimizing communication between wearable devices and routers for real-time data transmission. This involved configuring the MQTT protocol to handle data spikes without delays or loss of information.
• Designing a scalable system capable of processing large volumes of data generated by hundreds of wearable devices deployed across multiple facilities.
• Creating a user-friendly interface that could be easily adopted by non-technical staff for monitoring and maintenance.

Tools and Technologies

A wide range of tools and technologies were employed to address these challenges:

• Python: Core programming language for data processing and model development. Python's versatility made it ideal for managing the end-to-end workflow, from data preprocessing to model deployment.
• TensorFlow: Used for building and training deep learning models. Specific architectures such as Convolutional Neural Networks (CNNs) were explored to handle time-series data from accelerometers.
• Scikit-learn (sklearn): Utilized for data preprocessing, feature extraction, and testing classical machine learning methods like Random Forest and SVM as baselines.
• Pandas: Essential for data manipulation and analysis. Data cleaning, merging, and exploratory data analysis were performed extensively using Pandas.
• PyQt5: Developed a custom desktop application to streamline data collection and labeling. The application allowed for efficient visualization of collected data, manual corrections, and annotations.
• MQTT Protocol: Facilitated efficient data transmission from wearables to the server. MQTT's lightweight design ensured reliability even with intermittent connectivity.
• Docker: Used to containerize the application, ensuring easy deployment across different environments and consistent performance.

Custom PyQt5 Application

As part of the project, I developed a PyQt5-based desktop application to simplify the process of data collection and labeling. This tool became an essential component in managing the vast amount of data generated during testing and real-world deployment. The application provided the following features:

• Data Visualization: Users could visualize accelerometer and gyroscope data in real-time, allowing for quick assessments of data quality.
• Manual Labeling: The application enabled manual corrections and annotations, ensuring that training datasets were accurate and reliable.
• Batch Processing: Large datasets could be processed efficiently, saving significant time compared to manual methods.
• User-Friendly Interface: Designed with simplicity in mind, the application was intuitive and required minimal training for new users.

This tool played a pivotal role in accelerating the data preparation phase of the project, enabling the team to focus more on model development and testing. It also demonstrated the importance of creating auxiliary tools to support core machine learning tasks.

Infrastructure and Workflow

The system was built on a robust infrastructure designed to handle real-time data flow. The workflow was structured as follows:

1. Wearable devices continuously collected acceleration and gyroscope data, providing a detailed view of movements in three dimensions.
2. Data was transmitted via MQTT to local routers, ensuring minimal latency. The routers acted as intermediaries, buffering data in case of network interruptions.
3. Routers forwarded the data to a central server for processing and storage. The server was equipped with redundant systems to ensure reliability and data integrity.
4. Machine learning models processed incoming data to detect potential falls. The models were optimized to run in real-time, making predictions within milliseconds of receiving data.
5. Alerts were generated in real-time for caregivers or healthcare staff. Alerts included details like the time of the fall, device ID, and location, enabling prompt response.

Additionally, the system incorporated data visualization dashboards, providing insights into patient activity levels, fall risk assessments, and overall system performance. These dashboards were instrumental in engaging stakeholders and demonstrating the system's impact.

Testing and Validation

Rigorous testing was conducted to validate the system's accuracy and robustness. This included:

• Simulating falls using a dummy to collect realistic data. Different fall scenarios, such as forward, backward, and sideward falls, were tested to ensure comprehensive model training.
• Testing the system with volunteers in controlled environments. These tests helped identify edge cases and improve the system's sensitivity and specificity.
• Analyzing edge cases, such as quick movements or abnormal postures, to reduce false positives. Activities like bending down or sudden arm movements were explicitly accounted for in the model.
• Iteratively refining the models based on feedback from tests. Each iteration improved the model's robustness and adaptability to diverse environments.

Conclusion and Impact

The Fall Detection System represents a significant step forward in leveraging technology to improve healthcare. The project provided invaluable insights into real-world machine learning applications and reinforced the importance of interdisciplinary collaboration. By combining advanced IoT infrastructure, robust machine learning models, and practical user interfaces, the system not only enhanced safety for elderly individuals but also paved the way for further innovation in healthcare monitoring.

Beyond technical achievements, the project underscored the importance of empathy in technology development. Each aspect of the system was designed with the end-user in mind, ensuring ease of use for caregivers and reliability for patients. This experience has inspired me to continue exploring ways to bridge the gap between technology and societal needs.