TLDR: The KIRETT project introduces a wearable device that uses artificial intelligence, specifically an artificial neural network and a knowledge graph, to improve first aid during rescue operations. It recognizes patient health situations in real-time, provides contextual treatment recommendations, and aims to reduce medical errors and increase survival rates. The system is built for reliability and deployed on an edge FPGA using Apache TVM/VTA.
The KIRETT project introduces a groundbreaking wearable device designed to significantly enhance first aid during critical rescue operations. This innovative system leverages artificial intelligence to provide real-time, computer-aided situation recognition and contextual recommendations to rescue personnel. The primary goal is to minimize patient harm from incorrect treatments and boost survival rates.
At its core, KIRETT employs an artificial neural network (ANN) to identify a patient’s health situation. This ANN is trained on extensive historical rescue records, including vital health information, initial diagnoses, first aid plans, and rescuer observations from Siegen-Wittgenstein, Germany. The data undergoes rigorous sorting and filtering using Python-based tools and is stored in a PostgreSQL database. The system identifies common complication groups such as pulmonary, cardiovascular, and neurological diseases, rather than specific individual diseases, to prevent overfitting and reduce latency.
Beyond situation recognition, KIRETT integrates a knowledge graph to advise on proper treatment paths. This graph is built by analyzing standard operating procedures and specialized treatment guidelines from the Siegen Rescue Station. Text mining techniques are used to extract crucial information, which is then modeled into an event-driven process chain within a Neo4j graph database. This database is stored directly on the wearable device.
The communication between the neural network and the knowledge graph is a key feature. The ANN continuously processes new data (like temperature or ECG readings) and provides a probability distribution of possible health situations. The main application then queries the knowledge graph for the most likely starting point for treatment. This allows the wearable to dynamically suggest treatment paths, adapting to changes in the patient’s condition and displaying these recommendations graphically on a touchscreen interface. Rescue personnel can manually adjust or interfere with these suggestions.
The wearable itself is designed for energy efficiency, real-time capability, and reliability. It accepts various inputs, including vital signs from medical devices (via Wi-Fi or Bluetooth), information from control centers, and direct inputs from paramedics through question-and-answer dialogues on its touchscreen. Outputs include situation recognition, patient severity classification, specialized treatment plans, and recommended actions.
For deploying the deep learning model, the project utilizes Apache TVM, an open-source toolchain, and the VTA (versatile tensor accelerator) on an edge FPGA. This choice prioritizes security, network stability, and local data privacy. Recognizing the critical nature of medical applications, the Apache TVM/VTA framework has been extended to ensure temporal predictability, reliability, and fault tolerance, incorporating concepts like redundancy and resource efficiency to prevent catastrophic outcomes in case of system failure.
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The KIRETT project represents a significant step towards integrating advanced AI into emergency medical services, promising to enhance the quality of care and increase the efficiency of rescue operations. For more details, you can refer to the original research paper. Read the full paper here.
