Ming-Chun Huang

2.8k total citations
92 papers, 2.0k citations indexed

About

Ming-Chun Huang is a scholar working on Biomedical Engineering, Computer Vision and Pattern Recognition and Physiology. According to data from OpenAlex, Ming-Chun Huang has authored 92 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Biomedical Engineering, 18 papers in Computer Vision and Pattern Recognition and 17 papers in Physiology. Recurrent topics in Ming-Chun Huang's work include Context-Aware Activity Recognition Systems (13 papers), Non-Invasive Vital Sign Monitoring (11 papers) and Muscle activation and electromyography studies (10 papers). Ming-Chun Huang is often cited by papers focused on Context-Aware Activity Recognition Systems (13 papers), Non-Invasive Vital Sign Monitoring (11 papers) and Muscle activation and electromyography studies (10 papers). Ming-Chun Huang collaborates with scholars based in United States, China and Taiwan. Ming-Chun Huang's co-authors include Wenyao Xu, Majid Sarrafzadeh, Jason J. Liu, Diliang Chen, Chih‐Ming Ho, Navid Amini, Peter B. Lillehoj, Nabil Alshurafa, Lei He and Haotian Jiang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Lab on a Chip and International Journal of Environmental Research and Public Health.

In The Last Decade

Ming-Chun Huang

89 papers receiving 2.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ming-Chun Huang United States 24 1.0k 428 267 260 252 92 2.0k
João Paulo Silva Cunha Portugal 28 791 0.8× 388 0.9× 208 0.8× 151 0.6× 730 2.9× 177 2.9k
Maurizio Schmid Italy 28 1.1k 1.1× 238 0.6× 205 0.8× 184 0.7× 477 1.9× 161 2.4k
Laurence Kenney United Kingdom 26 1.8k 1.7× 641 1.5× 157 0.6× 135 0.5× 385 1.5× 111 2.8k
Shyamal Patel United States 22 1.6k 1.5× 578 1.4× 453 1.7× 143 0.6× 324 1.3× 63 3.2k
Andrea Mannini Italy 25 1.2k 1.2× 845 2.0× 215 0.8× 119 0.5× 237 0.9× 108 2.5k
Sarah Ostadabbas United States 21 561 0.5× 389 0.9× 111 0.4× 148 0.6× 191 0.8× 106 1.5k
Nabil Alshurafa United States 24 644 0.6× 601 1.4× 193 0.7× 206 0.8× 107 0.4× 97 1.9k
Frank Knoefel Canada 24 650 0.6× 506 1.2× 167 0.6× 81 0.3× 258 1.0× 185 2.2k
Gearóid ÓLaighin Ireland 33 1.3k 1.3× 1.0k 2.4× 153 0.6× 156 0.6× 200 0.8× 107 4.1k
Alessandro Tognetti Italy 28 1.6k 1.6× 346 0.8× 484 1.8× 350 1.3× 463 1.8× 141 2.6k

Countries citing papers authored by Ming-Chun Huang

Since Specialization
Citations

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

Fields of papers citing papers by Ming-Chun Huang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ming-Chun Huang. 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 Ming-Chun Huang. The network helps show where Ming-Chun Huang may publish in the future.

Co-authorship network of co-authors of Ming-Chun Huang

This figure shows the co-authorship network connecting the top 25 collaborators of Ming-Chun Huang. A scholar is included among the top collaborators of Ming-Chun Huang 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 Ming-Chun Huang. Ming-Chun Huang 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.
Schnall, Rebecca, et al.. (2025). Feasibility and Acceptability of the Sense2Quit App for Improving Smoking Cessation in PWH. AIDS and Behavior. 29(6). 1920–1929.
2.
Huang, Ming-Chun, et al.. (2024). Multimodal speech recognition using EEG and audio signals: A novel approach for enhancing ASR systems. Smart Health. 32. 100477–100477. 3 indexed citations
3.
Hu, Jiajie, Ming-Chun Huang, & Xiong Yu. (2023). Deep learning based on connected vehicles for icing pavement detection. 2(1). 11 indexed citations
4.
Liu, Tiantian, Feng Lin, Chenhan Xu, et al.. (2023). WavoID: Robust and Secure Multi-modal User Identification via mmWave-voice Mechanism. 1–15. 8 indexed citations
5.
Liu, Tiantian, et al.. (2023). Wavoice: An mmWave-Assisted Noise-Resistant Speech Recognition System. ACM Transactions on Sensor Networks. 20(4). 1–29. 2 indexed citations
6.
Schnall, Rebecca, et al.. (2023). Theoretically Guided Iterative Design of the Sense2Quit App for Tobacco Cessation in Persons Living with HIV. International Journal of Environmental Research and Public Health. 20(5). 4219–4219. 4 indexed citations
7.
Jia, Haomiao, Patricia A. Cioe, Ming-Chun Huang, et al.. (2023). Pilot Testing of an mHealth App for Tobacco Cessation in People Living With HIV: Protocol for a Pilot Randomized Controlled Trial. JMIR Research Protocols. 12. e49558–e49558. 3 indexed citations
8.
Chen, Huan, et al.. (2022). Transfer Learning Model Knowledge Across Multi-Sensors Locations Over Body Sensor Network. IEEE Sensors Journal. 22(11). 10663–10670. 4 indexed citations
9.
Schnall, Rebecca, et al.. (2022). A Smoking Cessation Mobile App for Persons Living With HIV: Preliminary Efficacy and Feasibility Study. JMIR Formative Research. 6(8). e28626–e28626. 15 indexed citations
10.
Li, Huining, et al.. (2022). A fitness training optimization system based on heart rate prediction under different activities. Methods. 205. 89–96. 11 indexed citations
11.
Cavuoto, Lora, et al.. (2021). A progressive prediction model towards home-based stroke rehabilitation programs. Smart Health. 23. 100239–100239. 8 indexed citations
12.
Hooper, Monica Webb, David B. Miller, Enrique Saldı́var, et al.. (2021). Randomized controlled trial testing a video-text tobacco cessation intervention among economically disadvantaged African American adults.. Psychology of Addictive Behaviors. 35(7). 769–777. 6 indexed citations
13.
Chen, Huan, et al.. (2021). Smoking Cessation System for Preemptive Smoking Detection. IEEE Internet of Things Journal. 9(5). 3204–3214. 13 indexed citations
14.
Cao, Huiyi, et al.. (2020). Designing Deep Reinforcement Learning Systems for Musculoskeletal Modeling and Locomotion Analysis Using Wearable Sensor Feedback. IEEE Sensors Journal. 20(16). 9274–9282. 11 indexed citations
15.
Chu, Kuo‐Chung, et al.. (2020). Using Mobile Electroencephalography and Actigraphy to Diagnose Attention-Deficit/Hyperactivity Disorder: Case-Control Comparison Study. JMIR Mental Health. 7(6). e12158–e12158. 8 indexed citations
16.
Hooper, Monica Webb, et al.. (2020). A context-adaptive smoking cessation system using videos. Smart Health. 19. 100148–100148. 1 indexed citations
17.
18.
Chen, Diliang, et al.. (2019). Bring Gait Lab to Everyday Life: Gait Analysis in Terms of Activities of Daily Living. IEEE Internet of Things Journal. 7(2). 1298–1312. 56 indexed citations
19.
Yeh, Shih‐Ching, Ming-Chun Huang, Pa‐Chun Wang, et al.. (2014). Machine learning-based assessment tool for imbalance and vestibular dysfunction with virtual reality rehabilitation system. Computer Methods and Programs in Biomedicine. 116(3). 311–318. 42 indexed citations
20.
Lillehoj, Peter B., et al.. (2013). Rapid electrochemical detection on a mobile phone. Lab on a Chip. 13(15). 2950–2950. 198 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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