Fall Detection Solution for Retirement Homes and Assisted Living Facilities

Introduction

In retirement homes and assisted living facilities, falls and accidents are unfortunately common, yet continuous in-person monitoring is resource-intensive. Darwin Edge developed an AI-powered automated fall detection solution with a key focus on safeguarding patient privacy.

Fall Detection Solution

Darwin Edge developed a computer vision based system to monitor patients in their rooms and send alerts in the event of a fall or accident. The solution used an in-room camera to capture video data, with computer vision algorithms deployed directly on the device. By performing real-time analysis on the camera itself, all data processing remains local, eliminating the need for cloud connectivity and safeguarding patient privacy.

Key features included:

• Person Detection and Body Pose Estimation: Leveraging computer vision algorithms, the system accurately detected individuals within the camera’s field of view and analyzed body posture, identifying standard activities such as standing, sitting, and lying down.
• Fall Detection: Through body-pose estimation, the system identified unusual or hazardous postures, such as falls, and triggers an immediate alert.
• Application Interface for Notifications: Alerts and notifications are sent to an application accessible on desktop, mobile, or tablet, allowing authorized personnel to respond promptly.
• Privacy-Focused Processing: All data processing occurs on-device, strictly maintaining patient privacy.

Technology Stack

The development involved selecting and optimizing cutting-edge object detection algorithms for face landmarks and body-pose estimation, which were adapted for edge AI on the Nvidia Jetson platforms.

Our operational prototype used:

• Framework: Darwin Edge’s proprietary framework for fast and accurate computer vision pipeline development and deployment.
• Hardware: Nvidia Jetson Xavier , NVIDIA Jetson Orin NX.
• Models: TensorRT, OpenCV, and Nvidia DLA for Deep Neural Networks.
• Programming: C/C++ for the integration of the models and implementation of the application layer

Improved Fall Detection

Darwin Edge’s solution demonstrated the possibility to provide continuous, real-time patient monitoring with strict privacy protection, enhancing safety, independence for residents and cost control for retirement homes and assisted living facilities.

• Real-Time Patient Monitoring: the platform continuously monitored patients, identifying normal and anomalous behaviors and triggering real-time alerts as needed.
• Enhanced Privacy and Independence: by processing data on-device, the solution maintains patient privacy while reducing the need for constant in-person supervision, promoting independent living for the elderly.
• Scalable and Adaptable Design: the solution has been optimized to ensure it can be implemented across various hardware configurations, meeting the diverse requirements in terms of power, memory and processing limits of compact devices like phones, cameras, and IoT systems.

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Darwin Edge has also developed a remote vital signs monitoring solution that uses computer vision and signal processing to accurately measure heart rate, breathing rate, heart rate variability, and oxygen saturation in real-time. This solution can be combined with fall detection to provide a complete monitoring solution for retirement homes and assisted living facilities.

Request a free consultation to explore how we can create tailored AI-driven monitoring systems for real-time and privacy-conscious enhanced patient care.