Ian Cleland

2.1k total citations
82 papers, 1.2k citations indexed

About

Ian Cleland is a scholar working on Computer Vision and Pattern Recognition, Demography and General Health Professions. According to data from OpenAlex, Ian Cleland has authored 82 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Computer Vision and Pattern Recognition, 19 papers in Demography and 18 papers in General Health Professions. Recurrent topics in Ian Cleland's work include Context-Aware Activity Recognition Systems (38 papers), Technology Use by Older Adults (18 papers) and Mobile Health and mHealth Applications (17 papers). Ian Cleland is often cited by papers focused on Context-Aware Activity Recognition Systems (38 papers), Technology Use by Older Adults (18 papers) and Mobile Health and mHealth Applications (17 papers). Ian Cleland collaborates with scholars based in United Kingdom, Spain and Sweden. Ian Cleland's co-authors include Chris Nugent, Sally McClean, Josef Hallberg, Kåre Synnes, Andrey Boytsov, Dewar Finlay, Basel Kikhia, Paul McCullagh, Federico Cruciani and Shuai Zhang and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and IEEE Access.

In The Last Decade

Ian Cleland

79 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ian Cleland United Kingdom 17 576 252 249 204 150 82 1.2k
Jit Biswas Singapore 22 520 0.9× 459 1.8× 261 1.0× 200 1.0× 67 0.4× 93 1.3k
Ivan Miguel Pires Portugal 23 488 0.8× 390 1.5× 302 1.2× 298 1.5× 192 1.3× 156 1.9k
Pepijn Van de Ven Ireland 20 575 1.0× 226 0.9× 472 1.9× 121 0.6× 126 0.8× 79 1.6k
Vincent Rialle France 15 902 1.6× 288 1.1× 401 1.6× 215 1.1× 182 1.2× 66 1.5k
Frank Knoefel Canada 24 506 0.9× 158 0.6× 650 2.6× 114 0.6× 145 1.0× 185 2.2k
Ramón Hervás Spain 20 467 0.8× 217 0.9× 152 0.6× 97 0.5× 93 0.6× 91 1.1k
M. Alwan United States 13 568 1.0× 142 0.6× 443 1.8× 118 0.6× 104 0.7× 23 1.1k
Josef Hallberg Sweden 16 435 0.8× 218 0.9× 169 0.7× 82 0.4× 70 0.5× 47 920
Ren-Guey Lee Taiwan 19 311 0.5× 341 1.4× 405 1.6× 89 0.4× 93 0.6× 71 1.4k
Éric Campo France 11 654 1.1× 407 1.6× 194 0.8× 93 0.5× 113 0.8× 60 1.4k

Countries citing papers authored by Ian Cleland

Since Specialization
Citations

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

Fields of papers citing papers by Ian Cleland

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ian Cleland

This figure shows the co-authorship network connecting the top 25 collaborators of Ian Cleland. A scholar is included among the top collaborators of Ian Cleland 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 Ian Cleland. Ian Cleland 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.
Ortíz‐Barrios, Miguel, Ian Cleland, Mark Donnelly, et al.. (2024). Integrated Approach Using Intuitionistic Fuzzy Multicriteria Decision-Making to Support Classifier Selection for Technology Adoption in Patients with Parkinson Disease: Algorithm Development and Validation. JMIR Rehabilitation and Assistive Technologies. 11. e57940–e57940.
2.
Cleland, Ian, et al.. (2024). Leveraging Large Language Models for Activity Recognition in Smart Environments. 1–8. 2 indexed citations
3.
Haseeb, Abdul, Ian Cleland, Chris Nugent, & James McLaughlin. (2024). Optimizing Machine Learning for ResourceConstrained Devices: A Comparative Analysis of Preprocessing Techniques and Machine Learning Algorithms. 1–5. 1 indexed citations
4.
Cruciani, Federico, et al.. (2024). Data Augmentation for Human Activity Recognition With Generative Adversarial Networks. IEEE Journal of Biomedical and Health Informatics. 28(4). 2350–2361. 12 indexed citations
5.
Wang, Xia, et al.. (2023). Mobile agent path planning under uncertain environment using reinforcement learning and probabilistic model checking. Knowledge-Based Systems. 264. 110355–110355. 13 indexed citations
6.
Hughes, Ciara, Lynn Dunwoody, Ian Cleland, et al.. (2023). The effectiveness of mindfulness alone compared to exercise and mindfulness on fatigue in women with gynaecology cancer (GEMS): Protocol for a randomised feasibility trial. PLoS ONE. 18(10). e0278252–e0278252. 1 indexed citations
7.
Chaurasia, Priyanka, Sally McClean, Chris Nugent, et al.. (2021). Modelling mobile-based technology adoption among people with dementia. Personal and Ubiquitous Computing. 26(2). 365–384. 6 indexed citations
8.
García-Constantino, Matías, et al.. (2021). Design and Implementation of a Smart Home in a Box to Monitor the Wellbeing of Residents With Dementia in Care Homes. Frontiers in Digital Health. 3. 798889–798889. 7 indexed citations
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11.
Giggins, Oonagh M., et al.. (2019). Unobtrusive Monitoring of Home-Based Post-Stroke Rehabilitation Exercises Using Heterogeneous Sensors.. 1 indexed citations
12.
Nugent, Chris, et al.. (2019). Impact Analysis of Erroneous Data on IoT Reliability. Ulster University Research Portal (Ulster University). 1908–1915. 4 indexed citations
13.
Cruciani, Federico, Anastasios Vafeiadis, Chris Nugent, et al.. (2019). Comparing CNN and Human Crafted Features for Human Activity Recognition. 960–967. 16 indexed citations
14.
Abu-Tair, Mamun, Pushpinder Kaur Chouhan, Ian Cleland, et al.. (2019). Evaluation of an IoT Framework for a Workplace Wellbeing Application. 1783–1788. 4 indexed citations
15.
Espinilla, Macarena, Javier Medina-Quero, Alberto Salguero, et al.. (2018). Human Activity Recognition from the Acceleration Data of a Wearable Device. Which Features Are More Relevant by Activities?. SHILAP Revista de lepidopterología. 1242–1242. 15 indexed citations
16.
Spinsante, Susanna, et al.. (2016). A Mobile Application for Easy Design and Testing of Algorithms to Monitor Physical Activity in the Workplace. Mobile Information Systems. 2016. 1–17. 18 indexed citations
17.
Cleland, Ian, et al.. (2016). Implementing Gamification Techniques To Support Collection Of Longitudinal Kinematic Data For Use In A Clinical Setting. Ulster University Research Portal (Ulster University). 1 indexed citations
18.
Nugent, Chris, Sally McClean, Ian Cleland, et al.. (2016). The Empowering Role of Mobile Apps in Behavior Change Interventions: The Gray Matters Randomized Controlled Trial. JMIR mhealth and uhealth. 4(3). e93–e93. 36 indexed citations
19.
Ni, Qin, et al.. (2016). Dynamic detection of window starting positions and its implementation within an activity recognition framework. Journal of Biomedical Informatics. 62. 171–180. 44 indexed citations
20.
Cleland, Ian, Chris Nugent, Sally McClean, et al.. (2016). Assessing app quality through expert peer review: A case study from the gray matters study. PubMed. 4 2. 4379–4382. 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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2026