Edison Thomaz

2.1k total citations
56 papers, 1.4k citations indexed

About

Edison Thomaz is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Edison Thomaz has authored 56 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Computer Vision and Pattern Recognition, 15 papers in Human-Computer Interaction and 12 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Edison Thomaz's work include Context-Aware Activity Recognition Systems (19 papers), Innovative Human-Technology Interaction (13 papers) and Nutritional Studies and Diet (10 papers). Edison Thomaz is often cited by papers focused on Context-Aware Activity Recognition Systems (19 papers), Innovative Human-Technology Interaction (13 papers) and Nutritional Studies and Diet (10 papers). Edison Thomaz collaborates with scholars based in United States, United Kingdom and Australia. Edison Thomaz's co-authors include Gregory D. Abowd, Irfan Essa, Daniel A. Epstein, James Fogarty, Felicia Cordeiro, Keum San Chun, Elizabeth Bales, Aman Parnami, Abdelkareem Bedri and Thad Starner and has published in prestigious journals such as The Journal of Urology, Sensors and Health Psychology.

In The Last Decade

Edison Thomaz

51 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Edison Thomaz United States 20 472 381 313 286 238 56 1.4k
Nabil Alshurafa United States 24 601 1.3× 206 0.5× 385 1.2× 330 1.2× 644 2.7× 97 1.9k
Miíkka Ermes Finland 20 928 2.0× 109 0.3× 193 0.6× 260 0.9× 586 2.5× 39 2.1k
Jennifer Boger Canada 26 550 1.2× 382 1.0× 74 0.2× 236 0.8× 277 1.2× 113 2.1k
Mashfiqui Rabbi United States 17 218 0.5× 375 1.0× 95 0.3× 393 1.4× 72 0.3× 31 1.7k
Haik Kalantarian United States 24 279 0.6× 65 0.2× 194 0.6× 167 0.6× 299 1.3× 53 1.5k
Juha Pärkkä Finland 20 1.1k 2.4× 189 0.5× 122 0.4× 318 1.1× 861 3.6× 42 2.4k
Robert Dickerson United States 16 221 0.5× 237 0.6× 192 0.6× 105 0.4× 78 0.3× 36 1.1k
Tauhidur Rahman United States 19 141 0.3× 164 0.4× 81 0.3× 131 0.5× 213 0.9× 88 1.1k
Jonathan Lester United States 16 711 1.5× 212 0.6× 85 0.3× 119 0.4× 264 1.1× 33 1.5k
Oscar Mayora Italy 21 291 0.6× 138 0.4× 39 0.1× 177 0.6× 135 0.6× 87 1.8k

Countries citing papers authored by Edison Thomaz

Since Specialization
Citations

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

Fields of papers citing papers by Edison Thomaz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Edison Thomaz

This figure shows the co-authorship network connecting the top 25 collaborators of Edison Thomaz. A scholar is included among the top collaborators of Edison Thomaz 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 Edison Thomaz. Edison Thomaz 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.
Thomaz, Edison, et al.. (2026). Detecting Eating Events with Inertial Sensing in a Ring Wearable. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. 10(1). 1–29.
2.
3.
Kim, Eric, et al.. (2025). AttenGluco: Multimodal Transformer-Based Blood Glucose Forecasting on AI-READI Dataset. PubMed. 2025. 1–7. 1 indexed citations
4.
Conroy, David E., et al.. (2024). Promoting fluid intake to increase urine volume for kidney stone prevention: Protocol for a randomized controlled efficacy trial of the sip intervention. Contemporary Clinical Trials. 138. 107454–107454. 4 indexed citations
5.
Choi, Eunsol, et al.. (2023). Understanding Postpartum Parents' Experiences via Two Digital Platforms. Proceedings of the ACM on Human-Computer Interaction. 7(CSCW1). 1–23. 4 indexed citations
6.
Thomaz, Edison, et al.. (2023). Automated Face-To-Face Conversation Detection on a Commodity Smartwatch with Acoustic Sensing. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. 7(3). 1–29. 4 indexed citations
7.
Streeper, Necole M., et al.. (2023). Feasibility of Mini sipIT Behavioral Intervention to Increase Urine Volume in Patients With Kidney Stones. Urology. 179. 39–43. 6 indexed citations
8.
Thomaz, Edison, et al.. (2023). Auditory chaos classification in real-world environments. Frontiers in Digital Health. 5. 1261057–1261057. 1 indexed citations
9.
Benge, Jared F., Alyssa Aguirre, Michael K. Scullin, et al.. (2023). Digital Methods for Performing Daily Tasks Among Older Adults: An Initial Report of Frequency of Use and Perceived Utility. Experimental Aging Research. 50(2). 133–154. 11 indexed citations
10.
Walls, Theodore A., et al.. (2022). A Conceptual Model for Mobile Health-enabled Slow Eating Strategies. Journal of Nutrition Education and Behavior. 55(2). 145–150. 1 indexed citations
11.
Streeper, Necole M., et al.. (2022). PD05-11 USE OF MINI SIP IT BEHAVIORAL INTERVENTION INCREASES FLUID INTAKE IN PATIENTS WITH KIDNEY STONES. The Journal of Urology. 207(Supplement 5).
12.
Lu, Xi, Edison Thomaz, & Daniel A. Epstein. (2022). Understanding People's Perceptions of Approaches to Semi-Automated Dietary Monitoring. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. 6(3). 1–27. 11 indexed citations
13.
Barczyk, Amanda N., R. Cameron Craddock, Gabriella M. Harari, et al.. (2021). Improving prediction of real-time loneliness and companionship type using geosocial features of personal smartphone data. Smart Health. 20. 100180–100180. 20 indexed citations
14.
Radhakrishnan, Kavita, Christine Julien, Tom Baranowski, et al.. (2021). Feasibility of a Sensor-Controlled Digital Game for Heart Failure Self-management: Randomized Controlled Trial. JMIR Serious Games. 9(4). e29044–e29044. 14 indexed citations
15.
Bell, Brooke M., Ridwan Alam, Nabil Alshurafa, et al.. (2020). Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review. npj Digital Medicine. 3(1). 38–38. 78 indexed citations
16.
Conroy, David E., et al.. (2020). Just-in-time adaptive intervention to promote fluid consumption in patients with kidney stones.. Health Psychology. 39(12). 1062–1069. 22 indexed citations
17.
18.
Chun, Keum San, et al.. (2019). Towards a generalizable method for detecting fluid intake with wrist-mounted sensors and adaptive segmentation. PubMed. 2019. 80–85. 25 indexed citations
19.
Dimiccoli, Mariella, Juan Marín, & Edison Thomaz. (2018). Mitigating Bystander Privacy Concerns in Egocentric Activity Recognition with Deep Learning and Intentional Image Degradation. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. 1(4). 1–18. 41 indexed citations
20.
Thomaz, Edison, Aman Parnami, Jonathan Bidwell, Irfan Essa, & Gregory D. Abowd. (2013). Technological approaches for addressing privacy concerns when recognizing eating behaviors with wearable cameras. 739–748. 38 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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