Madhuri Panwar

1.1k total citations
12 papers, 811 citations indexed

About

Madhuri Panwar is a scholar working on Biomedical Engineering, Cardiology and Cardiovascular Medicine and Computer Vision and Pattern Recognition. According to data from OpenAlex, Madhuri Panwar has authored 12 papers receiving a total of 811 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Biomedical Engineering, 4 papers in Cardiology and Cardiovascular Medicine and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Madhuri Panwar's work include Non-Invasive Vital Sign Monitoring (5 papers), ECG Monitoring and Analysis (3 papers) and Muscle activation and electromyography studies (3 papers). Madhuri Panwar is often cited by papers focused on Non-Invasive Vital Sign Monitoring (5 papers), ECG Monitoring and Analysis (3 papers) and Muscle activation and electromyography studies (3 papers). Madhuri Panwar collaborates with scholars based in India, Belgium and Australia. Madhuri Panwar's co-authors include Amit Acharyya, Dwaipayan Biswas, Arvind Gautam, Koushik Maharatna, Nick Van Helleputte, Chris Van Hoof, Mario Konijnenburg, Luke Everson, Chris H. Kim and Shrishail Patki and has published in prestigious journals such as IEEE Transactions on Biomedical Engineering, IEEE Sensors Journal and IEEE Transactions on Biomedical Circuits and Systems.

In The Last Decade

Madhuri Panwar

12 papers receiving 790 citations

Peers

Madhuri Panwar

CCM

SURGE

CVPR

Deepak Joshi India

Wala Saadeh Pakistan

Patrick Kugler Germany

Fangmin Sun China

Daniele Esposito Italy

Arash Arami Canada

Hoda Mohammadzade Iran

R. Yousefi United States

Jianchu Yao United States

Cristian Rotariu Romania

Deepak Joshi India

Madhuri Panwar

526 ×0.9

56 ×0.2

CCM

103 ×0.7

71 ×0.5

SURGE

68 ×0.5

CVPR

Citations per year, relative to Madhuri Panwar Madhuri Panwar (= 1×) peers Deepak Joshi

Countries citing papers authored by Madhuri Panwar

Since Specialization

Citations

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

Fields of papers citing papers by Madhuri Panwar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Madhuri Panwar

This figure shows the co-authorship network connecting the top 25 collaborators of Madhuri Panwar. A scholar is included among the top collaborators of Madhuri Panwar 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 Madhuri Panwar. Madhuri Panwar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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12 of 12 papers shown

Gautam, Arvind, Madhuri Panwar, Sridhar P. Arjunan, et al.. (2020). Locomo-Net: A Low -Complex Deep Learning Framework for sEMG-Based Hand Movement Recognition for Prosthetic Control. IEEE Journal of Translational Engineering in Health and Medicine. 8. 1–12. 49 indexed citations

Gautam, Arvind, Madhuri Panwar, Dwaipayan Biswas, & Amit Acharyya. (2020). MyoNet: A Transfer-Learning-Based LRCN for Lower Limb Movement Recognition and Knee Joint Angle Prediction for Remote Monitoring of Rehabilitation Progress From sEMG. IEEE Journal of Translational Engineering in Health and Medicine. 8. 1–10. 86 indexed citations

Panwar, Madhuri, et al.. (2020). M2DA: A Low-Complex Design Methodology for Convolutional Neural Network Exploiting Data Symmetry and Redundancy. Circuits Systems and Signal Processing. 40(3). 1542–1567. 3 indexed citations

Panwar, Madhuri, Arvind Gautam, Dwaipayan Biswas, & Amit Acharyya. (2020). PP-Net: A Deep Learning Framework for PPG-Based Blood Pressure and Heart Rate Estimation. IEEE Sensors Journal. 20(17). 10000–10011. 169 indexed citations

Panwar, Madhuri, et al.. (2020). CardioNet: Deep Learning Framework for Prediction of CVD Risk Factors. 1–5. 10 indexed citations

Panwar, Madhuri, Dwaipayan Biswas, Michael Jöbges, et al.. (2019). Rehab-Net: Deep Learning Framework for Arm Movement Classification Using Wearable Sensors for Stroke Rehabilitation. IEEE Transactions on Biomedical Engineering. 66(11). 3026–3037. 107 indexed citations

Biswas, Dwaipayan, Luke Everson, Muqing Liu, et al.. (2019). CorNET: Deep Learning Framework for PPG-Based Heart Rate Estimation and Biometric Identification in Ambulant Environment. IEEE Transactions on Biomedical Circuits and Systems. 13(2). 282–291. 216 indexed citations

Everson, Luke, Dwaipayan Biswas, Madhuri Panwar, et al.. (2018). BiometricNet: Deep Learning based Biometric Identification using Wrist-Worn PPG. 1–5. 44 indexed citations

Panwar, Madhuri, et al.. (2017). CNN based approach for activity recognition using a wrist-worn accelerometer. PubMed. 2017. 2438–2441. 90 indexed citations

10.

Panwar, Madhuri, et al.. (2017). Modified distributed arithmetic based low complexity CNN architecture design methodology. 1–4. 14 indexed citations

11.

Gautam, Arvind, et al.. (2017). Shape memory effect of nano-ferromagnetic particle doped NiTi for orthopedic devices and rehabilitation techniques. PubMed. 158. 950–953. 2 indexed citations

12.

Panwar, Madhuri, Amit Acharyya, Rishad Shafik, & Dwaipayan Biswas. (2016). K-nearest neighbor based methodology for accurate diagnosis of diabetes mellitus. 132–136. 21 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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