Peter Karsmakers

1.4k citations
68 papers · 1.1k · h-index 18

Impact in

Papers in

Peter Karsmakers

59 papers receiving 1.1k citations

Peers

Peter Karsmakers
Comparison fields: 5 of 120
  • Signal Processing 268
  • Computer Vision and Pattern Recognition 311
  • Biomedical Engineering 345
  • Artificial Intelligence 244
  • Computational Mathematics 4
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Sugata Munshi India
Sumair Aziz Pakistan
Zhongbo Sun China
Barathram Ramkumar India
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Citations per year

Countries citing papers authored by Peter Karsmakers

Since Specialization
Citations

This map shows the geographic impact of Peter Karsmakers'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 Peter Karsmakers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Karsmakers more than expected).

Fields of papers citing papers by Peter Karsmakers

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peter Karsmakers. 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 Peter Karsmakers. The network helps show where Peter Karsmakers may publish in the future.

Co-authors

The 25 scholars most cited alongside Peter Karsmakers, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Peter Karsmakers Line = papers co-authored together Peter Karsmakers links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 68 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2013151
2
LS-SVMlab Toolbox User's Guide version 1.7
2010150
3 201479
4 201368
5
The SINS database for detection of daily activities in a home environment using an Acoustic Sensor Network
201764
6 201356
7 201251
8 200745
9 201234
10
Automatic in-door fall detection based on microwave radar measurements
201234
11 201329
12 200927
13 201326
14
Automatic Monitoring of Activities of Daily Living based on Real-life Acoustic Sensor Data: a~preliminary study
201323
15 201121
16 202520
17 201118
18 201918
19 201117
20 202410

About Peter Karsmakers

Peter Karsmakers is a scholar working on Biomedical Engineering, Computer Vision and Pattern Recognition, Signal Processing, Artificial Intelligence and Surgery, having authored 68 papers that have together received 1.1k indexed citations. Recurring topics across this work include Non-Invasive Vital Sign Monitoring (16 papers), Speech and Audio Processing (14 papers), Music and Audio Processing (12 papers), Context-Aware Activity Recognition Systems (11 papers), Anomaly Detection Techniques and Applications (9 papers), Healthcare Technology and Patient Monitoring (9 papers), Music Technology and Sound Studies (6 papers) and Gait Recognition and Analysis (6 papers). The work is most often cited by research in Signal Processing (268 citations), Computer Vision and Pattern Recognition (311 citations), Biomedical Engineering (345 citations), Artificial Intelligence (244 citations) and Computational Mathematics (4 citations). Peter Karsmakers has collaborated with scholars based in Belgium, Malaysia and United Kingdom. Frequent co-authors include Bart Vanrumste, Johan A. K. Suykens, Dominique Schreurs, Kristiaan Pelckmans, M. Mercuri, Paul Leroux, Hugo Van hamme, Ping Jack Soh, Guy A. E. Vandenbosch and Kris De Brabanter. Their work appears in journals such as IEEE Access, Journal of the Audio Engineering Society, Machine Learning, IEEE Journal of Biomedical and Health Informatics and Information Fusion.

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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