The science
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At Hinlab, we strive for constant innovation. These are the key technologies which make our products stand out from the competition.

Surgeon putting on her mask

Physiological measurement & diagnostics

Hinlab uses the latest advances in optical technologies. We have partnered with leading researchers to ensure our use of PPG (photoplethysmography) is world class. Creatively applying advanced algorithms, we provide monitoring that meets the most stringent requirements. Our creativity is such that we have started developing a portfolio of patents which will be particularly valuable in the coming years.

Computer screen with a graph on

The internet of things (IoT)

Mastering the IoT for medical devices is in our DNA. From energy consumption to usability, we design and manufacture devices that run on low power, yet provide sustained functionality over long periods of time. Cybersecurity is built-in. From the communication protocols to the charging technologies that we choose, we consider every single feature of every electronic and firmware component of our products.

Stethoscope on money

Hardware & Software

Medical devices must meet the most rigorous standards, from design to manufacturing. Hinlab’s Quality Management System leans on a particularly powerful system which documents and tracks every progression through the development process. Thorough and reliable, it rapidly documents all the required processes, including risk management and unit testing.

Stethoscope on money

Artificial Intelligence (AI)

Hinlab’s products use AI to monitor patients’ health parameters and predict potential health complications. Our AI-powered diagnostics and smart alarms provide doctors with advanced tools to monitor the health status of patients. By continuously extracting key biomarkers from multiple vital signs, our data-driven technology paves the way for the future of personalized medicine. Our machine learning algorithms have a great impact on the prevention and early detection of cardiovascular and respiratory diseases. Similarly, the deterioration of patients can be predicted more rapidly, via pattern recognition of biosignal trend variation.

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