Faculty of Electrical Engineering and Information Technologies

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Author: search.filters.author.Kalabakov, StefanHas files: No

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    Federated Learning for Activity Recognition: A System Level Perspective
    (IEEE, 2023-04)
    Kalabakov, Stefan
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    Jovanovski, Borche
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    Rakovic, Valentin
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    Pfitzner, Bjarne
    The past decade has seen substantial growth in the prevalence and capabilities of wearable devices. For instance, recent human activity recognition (HAR) research has explored using wearable devices in applications such as remote monitoring of patients, detection of gait abnormalities, and cognitive disease identification. However, data collection poses a major challenge in developing HAR systems, especially because of the need to store data at a central location. This raises privacy concerns and makes continuous data collection difficult and expensive due to the high cost of transferring data from a user’s wearable device to a central repository. Considering this, we explore the adoption of federated learning (FL) as a potential solution to address the privacy and cost issues associated with data collection in HAR. More specifically, we investigate the performance and behavioral differences between FL and deep learning (DL) HAR models, under various conditions relevant to real-world deployments. Namely, we explore the differences between the two types of models when (i) using data from different sensor placements, (ii) having access to users with data from heterogeneous sensor placements, (iii) considering bandwidth efficiency, and (iv) dealing with data with incorrect labels. Our results show that FL models suffer from a consistent performance deficit in comparison to their DL counterparts, but achieve these results with much better bandwidth efficiency. Furthermore, we observe that FL models exhibit very similar responses to those of DL models when exposed to data from heterogeneous sensor placements. Finally, we show that the FL models are more robust to data with incorrect labels than their centralized DL counterparts.
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    Prediction of Hospital Readmission using Federated Learning
    (IEEE, 2023-06-27)
    Sazdov, Borjan
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    Tashkovska, Matea
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    Krsteski, Stefan
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    Jovanovski, Borche
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    Kalabakov, Stefan
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    SONAR, a nursing activity dataset with inertial sensors
    (Springer Science and Business Media LLC, 2023-10-20)
    Konak, Orhan
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    Döring, Valentin
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    Fiedler, Tobias
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    Liebe, Lucas
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    Masopust, Leander
    <jats:title>Abstract</jats:title><jats:p>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.</jats:p>
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    Head-AR: Human Activity Recognition with Head-Mounted IMU Using Weighted Ensemble Learning
    (Springer Singapore, 2020-12-24)
    Gjoreski, Hristijan
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    Kiprijanovska, Ivana
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    Stankoski, Simon
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    Kalabakov, Stefan
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    Broulidakis, John