2023

Federated Learning for Activity Recognition: A System Level Perspective. Kalabakov, Stefan; Jovanovski, Borche; Denkovski, Daniel; Rakovic, Valentin; Pfitzner, Bjarne; Konak, Orhan; Arnrich, Bert; Gjoreski, Hristijan in IEEE Access (2023). 11 64442–64457.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]
 
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.
@article{kalabakov2023federated, abstract = {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.}, author = {Kalabakov, Stefan and Jovanovski, Borche and Denkovski, Daniel and Rakovic, Valentin and Pfitzner, Bjarne and Konak, Orhan and Arnrich, Bert and Gjoreski, Hristijan}, journal = {IEEE Access}, keywords = {bertarnrich bjarnepfitzner orhankonak stefankalabakov widehealth}, pages = {64442-64457}, title = {Federated Learning for Activity Recognition: A System Level Perspective}, volume = 11, year = 2023 }

HARE: Unifying the Human Activity Recognition Engineering Workflow. Konak, Orhan; van de Water, Robin; Döring, Valentin; Fiedler, Tobias; Liebe, Lucas; Masopust, Leander; Postnov, Kirill; Sauerwald, Franz; Treykorn, Felix; Wischmann, Alexander; Gjoreski, Hristijan; Luštrek, Mitja; Arnrich, Bert in Sensors (2023). 23(23)
[ BibTeX ] [ URL ] [ DOI ]
 
@article{s23239571, author = {Konak, Orhan and van de Water, Robin and Döring, Valentin and Fiedler, Tobias and Liebe, Lucas and Masopust, Leander and Postnov, Kirill and Sauerwald, Franz and Treykorn, Felix and Wischmann, Alexander and Gjoreski, Hristijan and Luštrek, Mitja and Arnrich, Bert}, journal = {Sensors}, keywords = {bertarnrich orhankonak robinvandewater widehealth}, number = 23, title = {HARE: Unifying the Human Activity Recognition Engineering Workflow}, volume = 23, year = 2023 }

Overcoming Data Scarcity in Human Activity Recognition. Konak, Orhan; Liebe, Lucas; Postnov, Kirill; Sauerwald, Franz; Gjoreski, Hristijan; Luštrek, Mitja; Arnrich, Bert in IEEE EMBC 2023 (2023).
[ BibTeX ]
 
@article{konak2023overcoming, author = {Konak, Orhan and Liebe, Lucas and Postnov, Kirill and Sauerwald, Franz and Gjoreski, Hristijan and Luštrek, Mitja and Arnrich, Bert}, booktitle = {IEEE EMBC 2023}, keywords = {bertarnrich orhankonak widehealth}, organization = {IEEE EMBC}, title = {Overcoming Data Scarcity in Human Activity Recognition}, year = 2023 }

SONAR, a nursing activity dataset with inertial sensors. Konak, Orhan; Döring, Valentin; Fiedler, Tobias; Liebe, Lucas; Masopust, Leander; Postnov, Kirill; Sauerwald, Franz; Treykorn, Felix; Wischmann, Alexander; Kalabakov, Stefan; others in Scientific Data (2023). 10(1) 727.
[ BibTeX ]
 
@article{konak2023sonar, author = {Konak, Orhan and D{ö}ring, Valentin and Fiedler, Tobias and Liebe, Lucas and Masopust, Leander and Postnov, Kirill and Sauerwald, Franz and Treykorn, Felix and Wischmann, Alexander and Kalabakov, Stefan and others}, journal = {Scientific Data}, keywords = {bertarnrich orhankonak stefankalabakov widehealth}, number = 1, pages = 727, publisher = {Nature Publishing Group UK London}, title = {SONAR, a nursing activity dataset with inertial sensors}, volume = 10, year = 2023 }