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Query: "author" (Lucas Liebe) .

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1.
SONAR, a nursing activity dataset with inertial sensors
Orhan Konak, Lucas Liebe, Kirill Postnov, Franz Sauerwald, Hristijan Gjoreski, Mitja Luštrek, Bert Arnrich, 2023, short scientific article

Abstract: Accurate and comprehensive nursing documentation is essential to ensure quality patient care. To streamline this process, we present SONAR, a publicly available dataset of nursing activities recorded using inertial sensors in a nursing home. The dataset includes 14 sensor streams, such as acceleration and angular velocity, and 23 activities recorded by 14 caregivers using five sensors for 61.7 hours. The caregivers wore the sensors as they performed their daily tasks, allowing for continuous monitoring of their activities. We additionally provide machine learning models that recognize the nursing activities given the sensor data. In particular, we present benchmarks for three deep learning model architectures and evaluate their performance using different metrics and sensor locations. Our dataset, which can be used for research on sensor-based human activity recognition in real-world settings, has the potential to improve nursing care by providing valuable insights that can identify areas for improvement, facilitate accurate documentation, and tailor care to specific patient conditions.
Keywords: nursing documentation, nursing activities, SONAR, sensors
Published in DiRROS: 11.12.2023; Views: 222; Downloads: 119
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2.
HARE : unifying the human activity recognition engineering workflow
Orhan Konak, Lucas Liebe, Kirill Postnov, Franz Sauerwald, Hristijan Gjoreski, Mitja Luštrek, Bert Arnrich, 2023, original scientific article

Abstract: Sensor-based human activity recognition is becoming ever more prevalent. The increasing importance of distinguishing human movements, particularly in healthcare, coincides with the advent of increasingly compact sensors. A complex sequence of individual steps currently characterizes the activity recognition pipeline. It involves separate data collection, preparation, and processing steps, resulting in a heterogeneous and fragmented process. To address these challenges, we present a comprehensive framework, HARE, which seamlessly integrates all necessary steps. HARE offers synchronized data collection and labeling, integrated pose estimation for data anonymization, a multimodal classification approach, and a novel method for determining optimal sensor placement to enhance classification results. Additionally, our framework incorporates real-time activity recognition with on-device model adaptation capabilities. To validate the effectiveness of our framework, we conducted extensive evaluations using diverse datasets, including our own collected dataset focusing on nursing activities. Our results show that HARE’s multimodal and on-device trained model outperforms conventional single-modal and offline variants. Furthermore, our vision-based approach for optimal sensor placement yields comparable results to the trained model. Our work advances the field of sensor-based human activity recognition by introducing a comprehensive framework that streamlines data collection and classification while offering a novel method for determining optimal sensor placement.
Keywords: human activity recognition, multimodal classification, privacy preservation, real-time classification, sensor placement
Published in DiRROS: 11.12.2023; Views: 233; Downloads: 87
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