Mads Møller Jensen
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Human-Computer Interaction top 2%
- Computer Networks and Communications top 10%
- Cognitive Neuroscience
- Co-authors
- Tobias SonneThor Siiger PrentowAllan StisenAnind K. DeySourav BhattacharyaHenrik BlunckMikkel Baun KjærgaardFlorian Mueller
- Topics
- Innovative Human-Technology Interaction (6 papers)Educational Games and Gamification (4 papers)Context-Aware Activity Recognition Systems (4 papers)
- Partner nations
- DenmarkAustraliaUnited States
In The Last Decade
Mads Møller Jensen
18 papers receiving 822 citations
Hit Papers
Peers
Comparison fields: 5 of 96
- Computer Vision and Pattern Recognition 387
- Artificial Intelligence 245
- Human-Computer Interaction 161
- Computer Networks and Communications 118
- Cognitive Neuroscience 97
Countries citing papers authored by Mads Møller Jensen
This map shows the geographic impact of Mads Møller Jensen'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 Mads Møller Jensen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mads Møller Jensen more than expected).
Fields of papers citing papers by Mads Møller Jensen
This network shows the impact of papers produced by Mads Møller Jensen. 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 Mads Møller Jensen. The network helps show where Mads Møller Jensen may publish in the future.
Co-authorship network of co-authors of Mads Møller Jensen
This figure shows the co-authorship network connecting the top 25 collaborators of Mads Møller Jensen. A scholar is included among the top collaborators of Mads Møller Jensen 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 Mads Møller Jensen. Mads Møller Jensen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 19 | |
| 2 | 25 | |
| 3 | 12 | |
| 4 | 22 | |
| 5 | 23 | |
| 6 | 8 | |
| 7 | 26 | |
| 8 | 93 | |
| 9 | 16 | |
| 10 | 4 | |
| 11 | Smart devices are different:Assessing and mitigating mobile sensing heterogeneities for activity recognition | 17 |
| 12 | 8 | |
| 13 | 18 | |
| 14 | Smart Devices are Differentbreakdown → | 479 |
| 15 | Interactive Football-Training Based on Rebounders with Hit Position Sensing and Audio-Visual Feedback. | 8 |
| 16 | 42 | |
| 17 | 18 | |
| 18 | 5 |
About Mads Møller Jensen
Mads Møller Jensen is a scholar working on Human-Computer Interaction, Developmental and Educational Psychology and Orthopedics and Sports Medicine, having authored 18 papers that have together received 843 indexed citations. Recurring topics across this work include Innovative Human-Technology Interaction (6 papers), Educational Games and Gamification (4 papers) and Context-Aware Activity Recognition Systems (4 papers). The work is most often cited by research in Human-Computer Interaction (161 citations), Computer Vision and Pattern Recognition (387 citations) and Artificial Intelligence (245 citations). Mads Møller Jensen has collaborated with scholars based in Denmark, Australia and United States. Frequent co-authors include Tobias Sonne, Thor Siiger Prentow, Allan Stisen, Anind K. Dey, Sourav Bhattacharya, Henrik Blunck, Mikkel Baun Kjærgaard, Florian Mueller, Kaj Grønbæk and Majken Kirkegaard Rasmussen. Their work appears in journals such as PeerJ, Computer Supported Cooperative Work (CSCW) and interactions.
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.