Kejia Wang

480 total citations
8 papers, 365 citations indexed

About

Kejia Wang is a scholar working on Physical Therapy, Sports Therapy and Rehabilitation, Computer Vision and Pattern Recognition and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Kejia Wang has authored 8 papers receiving a total of 365 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Physical Therapy, Sports Therapy and Rehabilitation, 4 papers in Computer Vision and Pattern Recognition and 3 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Kejia Wang's work include Balance, Gait, and Falls Prevention (5 papers), Context-Aware Activity Recognition Systems (4 papers) and Injury Epidemiology and Prevention (2 papers). Kejia Wang is often cited by papers focused on Balance, Gait, and Falls Prevention (5 papers), Context-Aware Activity Recognition Systems (4 papers) and Injury Epidemiology and Prevention (2 papers). Kejia Wang collaborates with scholars based in Australia and Ireland. Kejia Wang's co-authors include Nigel H. Lovell, Stephen J. Redmond, Kim Delbaere, Matthew A. Brodie, Stephen R. Lord, Michael Del Rosario, Michela Persiani, Milou J. M. Coppens, Daina L. Sturnieks and Yves J. Gschwind and has published in prestigious journals such as IEEE Transactions on Biomedical Engineering, Sensors and IEEE Journal of Biomedical and Health Informatics.

In The Last Decade

Kejia Wang

8 papers receiving 359 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kejia Wang Australia	8	197	137	124	68	53	8	365
Nienke M. Kosse Netherlands	14	180 0.9×	50 0.4×	101 0.8×	64 0.9×	79 1.5×	27	615
Tal Shany Australia	7	136 0.7×	167 1.2×	152 1.2×	45 0.7×	32 0.6×	8	374
Lars Schwickert Germany	12	235 1.2×	249 1.8×	240 1.9×	30 0.4×	66 1.2×	18	494
Omar Aziz Canada	11	198 1.0×	308 2.2×	283 2.3×	41 0.6×	28 0.5×	27	543
Mareike Schulze Germany	12	80 0.4×	69 0.5×	73 0.6×	61 0.9×	34 0.6×	29	423
S. Nicolai Germany	9	206 1.0×	114 0.8×	144 1.2×	22 0.3×	97 1.8×	17	402
Y Y Sitoh Singapore	13	185 0.9×	66 0.5×	90 0.7×	61 0.9×	123 2.3×	21	635
Rachel Senden Netherlands	14	311 1.6×	65 0.5×	291 2.3×	57 0.8×	128 2.4×	41	754
Joana Silva Portugal	10	100 0.5×	193 1.4×	140 1.1×	19 0.3×	32 0.6×	30	339
C. Becker Germany	7	270 1.4×	361 2.6×	317 2.6×	28 0.4×	45 0.8×	7	596

Countries citing papers authored by Kejia Wang

Since Specialization

Citations

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

Fields of papers citing papers by Kejia Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kejia Wang

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

All Works

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8 of 8 papers shown

Lovell, Nigel H., et al.. (2020). Deep Learning for Activity Recognition in Older People Using a Pocket-Worn Smartphone. Sensors. 20(24). 7195–7195. 22 indexed citations

Wang, Kejia, Kim Delbaere, Matthew A. Brodie, et al.. (2017). Differences Between Gait on Stairs and Flat Surfaces in Relation to Fall Risk and Future Falls. IEEE Journal of Biomedical and Health Informatics. 21(6). 1479–1486. 54 indexed citations

Wang, Kejia, et al.. (2017). A case report of primary necrotising fasciitis of the breast. International Journal of Surgery Case Reports. 31(C). 221–224. 22 indexed citations

Brodie, Matthew A., Milou J. M. Coppens, Stephen R. Lord, et al.. (2015). Wearable pendant device monitoring using new wavelet-based methods shows daily life and laboratory gaits are different. Medical & Biological Engineering & Computing. 54(4). 663–674. 123 indexed citations

Shany, Tal, Kejia Wang, Ying Liu, Nigel H. Lovell, & Stephen J. Redmond. (2015). Review: Are we stumbling in our quest to find the best predictor? Over‐optimism in sensor‐based models for predicting falls in older adults. Healthcare Technology Letters. 2(4). 79–88. 41 indexed citations

Brodie, Matthew A., Kejia Wang, Kim Delbaere, et al.. (2015). New Methods to Monitor Stair Ascents Using a Wearable Pendant Device Reveal How Behavior, Fear, and Frailty Influence Falls in Octogenarians. IEEE Transactions on Biomedical Engineering. 62(11). 2595–2601. 24 indexed citations

Rosario, Michael Del, Kejia Wang, Jingjing Wang, et al.. (2014). A comparison of activity classification in younger and older cohorts using a smartphone. Physiological Measurement. 35(11). 2269–2286. 64 indexed citations

Wang, Kejia, Nigel H. Lovell, Michael Del Rosario, et al.. (2014). Inertial measurements of free-living activities: Assessing mobility to predict falls. PubMed. 83. 6892–6895. 15 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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