{"id":2388,"date":"2024-10-14T09:46:53","date_gmt":"2024-10-14T09:46:53","guid":{"rendered":"https:\/\/dataninja.nrw\/?page_id=2388"},"modified":"2025-02-10T09:16:08","modified_gmt":"2025-02-10T09:16:08","slug":"rl3-representation-reinforcement-and-rule-learning-2","status":"publish","type":"page","link":"https:\/\/dataninja.nrw\/?page_id=2388","title":{"rendered":"RoSe: Robust Individualization of Smart Sensor Technology through Transfer Learning Based Feature Selection"},"content":{"rendered":"
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The faces behind project RoSe<\/h3>\n\n\n\n
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<\/a>Nils L\u00f6wen<\/a><\/h4>\n\n\n\n

Hochschule Bielefeld<\/p>\n<\/div>\n\n\n\n

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Markus Vieth<\/a><\/h4>\n\n\n\n

Bielefeld University<\/p>\n<\/div>\n<\/div>\n\n\n<\/div>\n\n\n

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Adaptive Helpers: Devices that Think and Support<\/h1>\n\n\n\n

Every person is different. Some are tall, some are short, some have more or less muscle, and even bones vary from person to person. These differences are very important for us in the RoSe project, because we work with sensors that are worn directly on the body and measure signals from it\u2014these are called biosignals. You might be familiar with this from a doctor using an ECG to check your heart.<\/p>\n\n\n\n

Based on these signals, we are developing two devices: First, we are working on a supportive frame worn on the elbow. This so-called exoskeleton detects the muscles’ movements and helps the arm move. It\u2019s useful for people who frequently lift heavy loads. <\/p>\n\n\n\n

Second, we are creating an insole for shoes, with built-in pressure sensors. This device constantly measures how much weight is being put on the foot or leg while walking. It can alert people after surgery or injury, protecting them from overloading the affected limb.<\/p>\n\n\n\n

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But since every person is different, we can\u2019t create a custom device for each individual. That would be too expensive and take too much time. Instead, we design these two devices to fit as many people as possible. We also program the devices to adjust to each person and always provide good results. We are researching how the information we learn from one person\u2019s feet and arms can be applied to others.<\/p>\n\n\n\n

For our devices, we use knowledge from doctors, for example, about muscles, and we also use artificial intelligence (AI). AI learns from lots of data and performs calculations, like how fast the arm moves when certain muscles contract. Combining both approaches helps us achieve more accurate results. In summary, we are developing devices that support people and work well for everyone.<\/p>\n\n\n\n

Cooperation<\/h3>\n\n\n\n
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Machine Learning Group<\/p><\/b><\/a>\n

Prof. Dr. Barbara Hammer<\/p><\/a>\n

PhD student: Markus Vieth<\/a><\/p>\n <\/div>\n<\/div>\n

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Embedded Systems and Biomechatronics Group<\/p><\/b><\/a>\n

Prof. Dr. Axel Schneider<\/p><\/a>\n

PhD student: Nils Grimmelsmann<\/a><\/p>\n <\/div>\n<\/div>\n\n\n\n

Project Publications<\/h3>\n\n\n\n