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Preisverleihung des Basic Track der Bremen Big Data Challenge 2025 zeichnet junge Programmiertalente aus. Mehr lesen
Die Zukunft der Medizin ist digital, und das ist auch wichtig! Ärzt:innen werden in ihrem Arbeitsalltag mit einer Flut von Daten konfrontiert aus denen sie eine Diagnose, eine Behandlungsplanung und eine individuelle Prognose für ihre Patent:innen ableiten müssen. Um mit dieser Datenmenge besser umgehen zu können, kann KI helfen. Die Aufgabe des MEVIS zeigt anhand des Krankheitsbilds „Herzinfakt“, wie eine KI dabei hilft die Daten zu verarbeiten und die Arbeit der Ärzt:innen erleichtert. Aber auch die Grenzen der Vorhersagen und Prognosen sollen gut überlegt werden.
Sending humans to Mars and exploring our red neighboring planet has long been a dream of space exploration. However, Mars differs from Earth in many ways. The lack of atmosphere, absence of liquid water, and high radiation levels currently make it impossible to send a crew on such a journey. Instead, Mars rovers are used, but even they face numerous challenges on the Martian surface, with no direct intervention possible from Earth. When the first humans step onto Mars in a few years, additional difficulties will arise. One of these is the scarcity of resources, which must be brought from Earth and carefully utilized and recycled on Mars.
To address the wide range of challenges presented by a Mars mission (with or without humans), ZARM is offering two tasks for the 2024/25 Challenge.
Everyday life is increasingly shaped by digital technologies, with smartphones playing a central role. They have become versatile and convenient companions that can be taken and used anywhere. A fascinating aspect of modern smartphones lies in the variety of integrated sensors, which often go unnoticed. However, these sensors perform crucial tasks and significantly enhance the usability of the device.
As part of this challenge, the focus is on motion detection using sensors. Particularly noteworthy is the IMU (Inertial Measurement Unit) which consists of an accelerometer that captures acceleration in three dimensions and a gyroscope , which measures rotational speed. The goal of this challenge is to classify the following simple motion patterns using recorded sensor data with a smartphone in hand:
Pro Woche stehen insgesamt 3 Einreichungen zur Verfügung.
In the first phase, the task is to collect sufficient raw data for the first three motion classes presented. ("Standing," "Walking," "Dropping") using a smartphone. The data collection process should be designed to be as strategic and efficient as possible to minimize or completely avoid the effort required for subsequent data annotation (see additional notes below).
After completing the data collection, the development of a model for classifying motion patterns begins as soon as the self-recorded data (classes 1-3) have been verified for accuracy and the remaining data (classes 4-12) have been made available.
Die Zukunft der Medizin ist digital, und das ist auch wichtig! Ärzt:innen werden in ihrem Arbeitsalltag mit einer Flut von Daten konfrontiert aus denen sie eine Diagnose, eine Behandlungsplanung und eine individuelle Prognose für ihre Patent:innen ableiten müssen. Um mit dieser Datenmenge besser umgehen zu können, kann KI helfen. Die Aufgabe des MEVIS zeigt anhand des Krankheitsbilds „Herzinfakt“, wie eine KI dabei hilft die Daten zu verarbeiten und die Arbeit der Ärzt:innen erleichtert. Aber auch die Grenzen der Vorhersagen und Prognosen sollen gut überlegt werden.
Everyday life is increasingly shaped by digital technologies, with smartphones playing a central role. They have become versatile and convenient companions that can be taken and used anywhere. A fascinating aspect of modern smartphones lies in the variety of integrated sensors, which often go unnoticed. However, these sensors perform crucial tasks and significantly enhance the usability of the device.
As part of this challenge, the focus is on motion detection using sensors. Particularly noteworthy is the IMU (Inertial Measurement Unit) which consists of an accelerometer that captures acceleration in three dimensions and a gyroscope , which measures rotational speed. The goal of this challenge is to classify the following simple motion patterns using recorded sensor data with a smartphone in hand:
Pro Woche stehen insgesamt 3 Einreichungen zur Verfügung.
In the first phase, the task is to collect sufficient raw data for the first three motion classes presented. ("Standing," "Walking," "Dropping") using a smartphone. The data collection process should be designed to be as strategic and efficient as possible to minimize or completely avoid the effort required for subsequent data annotation (see additional notes below).
After completing the data collection, the development of a model for classifying motion patterns begins as soon as the self-recorded data (classes 1-3) have been verified for accuracy and the remaining data (classes 4-12) have been made available.
Everyday life is increasingly shaped by digital technologies, with smartphones playing a central role. They have become versatile and convenient companions that can be taken and used anywhere. A fascinating aspect of modern smartphones lies in the variety of integrated sensors, which often go unnoticed. However, these sensors perform crucial tasks and significantly enhance the usability of the device.
As part of this challenge, the focus is on motion detection using sensors. Particularly noteworthy is the IMU (Inertial Measurement Unit) which consists of an accelerometer that captures acceleration in three dimensions and a gyroscope , which measures rotational speed. The goal of this challenge is to classify the following simple motion patterns using recorded sensor data with a smartphone in hand:
Pro Woche stehen insgesamt 3 Einreichungen zur Verfügung.
In the first phase, the task is to collect sufficient raw data for the first three motion classes presented. ("Standing," "Walking," "Dropping") using a smartphone. The data collection process should be designed to be as strategic and efficient as possible to minimize or completely avoid the effort required for subsequent data annotation (see additional notes below).
After completing the data collection, the development of a model for classifying motion patterns begins as soon as the self-recorded data (classes 1-3) have been verified for accuracy and the remaining data (classes 4-12) have been made available.
