Michelle Karg

1.0k total citations
23 papers, 584 citations indexed

About

Michelle Karg is a scholar working on Computer Vision and Pattern Recognition, Biomedical Engineering and Artificial Intelligence. According to data from OpenAlex, Michelle Karg has authored 23 papers receiving a total of 584 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Vision and Pattern Recognition, 13 papers in Biomedical Engineering and 10 papers in Artificial Intelligence. Recurrent topics in Michelle Karg's work include Human Pose and Action Recognition (10 papers), Gait Recognition and Analysis (9 papers) and Anomaly Detection Techniques and Applications (9 papers). Michelle Karg is often cited by papers focused on Human Pose and Action Recognition (10 papers), Gait Recognition and Analysis (9 papers) and Anomaly Detection Techniques and Applications (9 papers). Michelle Karg collaborates with scholars based in Canada, Germany and Japan. Michelle Karg's co-authors include Dana Kulić, Kolja Kühnlenz, Jesse Hoey, Martin Buss, Jonathan Feng-Shun Lin, Rob Gorbet, Gentiane Venture, Shehroz S. Khan, Wolfgang Seiberl and Vincent Bonnet and has published in prestigious journals such as Applied Soft Computing, IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) and IEEE Transactions on Neural Systems and Rehabilitation Engineering.

In The Last Decade

Michelle Karg

22 papers receiving 563 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Michelle Karg Canada 11 268 216 130 127 120 23 584
Kerem Altun Türkiye 6 312 1.2× 208 1.0× 35 0.3× 163 1.3× 24 0.2× 12 559
Maria Kyrarini United States 13 133 0.5× 128 0.6× 123 0.9× 121 1.0× 18 0.1× 46 574
Thanassis Rikakis United States 18 257 1.0× 136 0.6× 71 0.5× 52 0.4× 19 0.2× 55 810
Matteo Spezialetti Italy 17 72 0.3× 121 0.6× 100 0.8× 71 0.6× 213 1.8× 36 782
Hitoshi Konosu Japan 10 60 0.2× 107 0.5× 53 0.4× 94 0.7× 129 1.1× 15 428
Frank Wallhoff Germany 14 301 1.1× 52 0.2× 96 0.7× 189 1.5× 117 1.0× 69 670
Jaeyeon Lee South Korea 13 285 1.1× 83 0.4× 49 0.4× 94 0.7× 27 0.2× 66 603
Raul Fernandez Rojas Australia 15 64 0.2× 228 1.1× 135 1.0× 64 0.5× 107 0.9× 51 756
Wen-Hung Chao Taiwan 7 111 0.4× 137 0.6× 81 0.6× 36 0.3× 225 1.9× 20 587
Andreas Aristidou Cyprus 20 790 2.9× 197 0.9× 88 0.7× 75 0.6× 89 0.7× 44 1.3k

Countries citing papers authored by Michelle Karg

Since Specialization
Citations

This map shows the geographic impact of Michelle Karg's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michelle Karg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michelle Karg more than expected).

Fields of papers citing papers by Michelle Karg

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michelle Karg. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michelle Karg. The network helps show where Michelle Karg may publish in the future.

Co-authorship network of co-authors of Michelle Karg

This figure shows the co-authorship network connecting the top 25 collaborators of Michelle Karg. A scholar is included among the top collaborators of Michelle Karg based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michelle Karg. Michelle Karg is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Bonnet, Vincent, et al.. (2017). Rhythmic Extended Kalman Filter for Gait Rehabilitation Motion Estimation and Segmentation. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 26(2). 407–418. 28 indexed citations
2.
Khan, Shehroz S., Michelle Karg, Dana Kulić, & Jesse Hoey. (2017). Detecting falls with X-Factor Hidden Markov Models. Applied Soft Computing. 55. 168–177. 22 indexed citations
3.
Karg, Michelle & Dana Kulić. (2017). Modeling Movement Primitives with Hidden Markov Models for Robotic and Biomedical Applications. Methods in molecular biology. 1552. 199–213. 2 indexed citations
4.
Karg, Michelle, et al.. (2017). Saliency-guided region proposal network for CNN based object detection. 1–8. 10 indexed citations
5.
Lin, Jonathan Feng-Shun, Michelle Karg, & Dana Kulić. (2016). Movement Primitive Segmentation for Human Motion Modeling: A Framework for Analysis. IEEE Transactions on Human-Machine Systems. 46(3). 325–339. 67 indexed citations
6.
Bonnet, Vincent, et al.. (2015). Rhythmic EKF for pose estimation during gait. 30. 1167–1172. 10 indexed citations
7.
Karg, Michelle, Gentiane Venture, Jesse Hoey, & Dana Kulić. (2014). Human Movement Analysis as a Measure for Fatigue: A Hidden Markov-Based Approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 22(3). 470–481. 43 indexed citations
8.
Karg, Michelle, et al.. (2014). Online tracking of the lower body joint angles using IMUs for gait rehabilitation. PubMed. 2014. 2310–2313. 19 indexed citations
9.
Karg, Michelle, et al.. (2014). Movement Analysis of Rehabilitation Exercises: Distance Metrics for Measuring Patient Progress. IEEE Systems Journal. 10(3). 1014–1025. 36 indexed citations
10.
Karg, Michelle, Wolfgang Seiberl, Florian Kreuzpointner, Johannes‐Peter Haas, & Dana Kulić. (2014). Clinical Gait Analysis: Comparing Explicit State Duration HMMs Using a Reference-Based Index. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 23(2). 319–331. 20 indexed citations
11.
Karg, Michelle, et al.. (2013). Body Movements for Affective Expression: A Survey of Automatic Recognition and Generation. IEEE Transactions on Affective Computing. 4(4). 341–359. 139 indexed citations
12.
Zhang, Tianxiang, Michelle Karg, Jonathan Feng-Shun Lin, Dana Kulić, & Gentiane Venture. (2013). IMU based single stride identification of humans. 3968. 220–225. 7 indexed citations
13.
Karg, Michelle, et al.. (2013). Human Movement Analysis: Extension of the F-Statistic to Time Series Using HMM. 3870–3875. 4 indexed citations
14.
Khan, Shehroz S., Michelle Karg, Jesse Hoey, & Dana Kulić. (2012). Towards the detection of unusual temporal events during activities using HMMs. 1075–1084. 21 indexed citations
15.
Karg, Michelle. (2012). Pattern Recognition Algorithms for Gait Analysis with Application to Affective Computing. mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich). 3 indexed citations
16.
Karg, Michelle, Kolja Kühnlenz, & Martin Buss. (2010). Recognition of Affect Based on Gait Patterns. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 40(4). 1050–1061. 96 indexed citations
17.
Karg, Michelle, et al.. (2010). Towards mapping emotive gait patterns from human to robot. 258–263. 10 indexed citations
18.
Karg, Michelle, Robert Jenke, Kolja Kühnlenz, & Martin Buss. (2009). A Two-fold PCA-Approach for Inter-Individual Recognition of Emotions in Natural Walking. mediaTUM (Technical University of Munich). 51–61. 9 indexed citations
19.
Karg, Michelle, Stephan Haug, Kolja Kühnlenz, & Martin Buss. (2009). A dynamic model and system-theoretic analysis of affect based on a Piecewise Linear system. 238–244. 3 indexed citations
20.
Karg, Michelle, et al.. (2008). Physiology and HRI: Recognition of over- and underchallenge. 448–452. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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