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Hello,
One slip, misstep, trip or stumble – and you're lying on the floor and, in the worst case, you've even injured yourself.
But what if we could recognize falls before they happen and thus avoid (serious) injuries and possibly even rehabilitation? In our latest video report, we show you exactly how this could become possible. In the SmartGAITLab at Koblenz University of Applied Sciences, researchers are working with sensors, controlled stumble training and AI to identify fall risks at an early stage.
Many people will benefit from this work, from sports and geriatrics to occupational health and safety. This includes, for example, a parcel service provider that is working with the researchers to better protect its employees from falls in their day-to-day work and reduce downtime.
Watch the video to see how the SmartGAITLab is researching fall prevention!
Your MEDICA editorial team
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Stents: AI follow-up care
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How is the heart doing after stent implantation? An AI can now answer this question, and faster than any manual evaluation. DeepNeo detects complications at an early stage and supports doctors with high precision. This development could make follow-up care easier, reduce follow-up operations and even save lives.
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Radiotherapy: AI detects kidney damage
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Radiotherapy for prostate cancer can have serious side effects, such as a deterioration in kidney function. A research team at the Technical University of Munich is now using AI to make such organ damage visible at an early stage. The AI analyzes tiny changes in the kidney on CT images and thus helps to prevent permanent damage.
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Tripping allowed – for more safety
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In the SmartGAITLab at Koblenz University of Applied Sciences, tripping is on purpose – and for good reason: researchers use sensors, AI and a clever setup to analyze early warning signs of falls. Prevention begins before the first misstep, whether in old age, at work or in sport. You want to see how modern research can help you get back on your feet? Don't miss this video!
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Prof. Thorsten Jungeblut is researching the topic of AI-supported smart homes with “KogniHome”. His project aims to collect as little data as possible using minimal sensor technology, while still ensuring the greatest possible safety for residents.
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