Pierluigi Casale
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence top 10%
- Biomedical Engineering
- Computer Networks and Communications
- Cardiology and Cardiovascular Medicine
- Co-authors
- Oriol PujolPetia RadevaMarco AltiniOliver AmftJulien PendersGuy PlasquiSally SinghJennifer Alison
- Topics
- Context-Aware Activity Recognition Systems (6 papers)Physical Activity and Health (4 papers)Anomaly Detection Techniques and Applications (4 papers)
- Cited by
- Computer Vision and Pattern RecognitionComplementary and alternative medicineArtificial Intelligence
- Partner nations
- NetherlandsGermanySpain
In The Last Decade
Pierluigi Casale
12 papers receiving 242 citations
Peers
Comparison fields: 5 of 65
- Computer Vision and Pattern Recognition 101
- Artificial Intelligence 95
- Biomedical Engineering 77
- Computer Networks and Communications 39
- Cardiology and Cardiovascular Medicine 36
Countries citing papers authored by Pierluigi Casale
This map shows the geographic impact of Pierluigi Casale'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 Pierluigi Casale with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pierluigi Casale more than expected).
Fields of papers citing papers by Pierluigi Casale
This network shows the impact of papers produced by Pierluigi Casale. 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 Pierluigi Casale. The network helps show where Pierluigi Casale may publish in the future.
Co-authorship network of co-authors of Pierluigi Casale
This figure shows the co-authorship network connecting the top 25 collaborators of Pierluigi Casale. A scholar is included among the top collaborators of Pierluigi Casale 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 Pierluigi Casale. Pierluigi Casale is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 20 | |
| 3 | 22 | |
| 4 | 10 | |
| 5 | 9 | |
| 6 | 8 | |
| 7 | 12 | |
| 8 | 2 | |
| 9 | 2 | |
| 10 | 38 | |
| 11 | 1 | |
| 12 | 121 |
About Pierluigi Casale
Pierluigi Casale is a scholar working on Computer Vision and Pattern Recognition, Complementary and alternative medicine and Physiology, having authored 12 papers that have together received 250 indexed citations. Recurring topics across this work include Context-Aware Activity Recognition Systems (6 papers), Physical Activity and Health (4 papers) and Anomaly Detection Techniques and Applications (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (101 citations), Complementary and alternative medicine (32 citations) and Artificial Intelligence (95 citations). Pierluigi Casale has collaborated with scholars based in Netherlands, Germany and Spain. Frequent co-authors include Oriol Pujol, Petia Radeva, Marco Altini, Oliver Amft, Julien Penders, Guy Plasqui, Sally Singh, Jennifer Alison, Ruth Tal‐Singer and Emiel F.�M. Wouters. Their work appears in journals such as Journal of Applied Physiology, Pattern Recognition and IEEE Internet of Things Journal.
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.