Rahm develops advanced monitoring solutions using patented sensor fusion technology tailored for human applications. Their systems integrate Doppler radar, machine learning, and computer vision for non-contact vital sign monitoring. This technology provides enhanced situational awareness and timely alerts in sectors like memory care, corrections, and neonatal intensive care.
Funding
Funding not disclosed
Founders
Product
Problem
Current methods for monitoring vital signs often require direct contact with the patient, causing discomfort and potentially limiting continuous, real-time data collection. This can be especially challenging in sensitive environments like neonatal intensive care units or correctional facilities, where physical contact needs to be minimized. The lack of continuous monitoring can delay the detection of critical health events or security breaches.
Solution
Rahm Sensor Development provides non-contact vital sign monitoring solutions using Frequency-Modulated Continuous-Wave (FMCW) radar technology and sensor fusion. Their Guardian solution enables continuous, remote monitoring of vital signs and movement without physical contact, offering enhanced situational awareness and proactive alerts. By leveraging Doppler radar, machine learning, and computer vision, the system accurately tracks real-time movement, predicts patient outcomes, detects irregular patterns, and automates routine tasks. The technology facilitates early detection of potential health issues or security threats, allowing for timely intervention and improved outcomes across various applications.
Target Audience
The primary target audience includes healthcare providers in neonatal intensive care units and memory care facilities, as well as correctional officers in correctional facilities, seeking continuous, non-invasive monitoring solutions to improve patient care and enhance safety.
Features
- Non-contact vital sign monitoring using FMCW radar technology
- Real-time movement and velocity tracking via Doppler radar
- Predictive analytics using machine learning to forecast patient outcomes and detect anomalies
- Computer vision for identifying anomalies and monitoring vital signs
- Customizable alerts based on pre-defined thresholds and behavioral pattern recognition
- Secure data transmission and access control with Ethernet and Wi-Fi support
- Continuous monitoring capabilities in all conditions through thermal vision and Power over Ethernet (PoE)
- Integration of video and audio feeds for enhanced situational awareness