The DOI matches its Crossref record; 84 of the 84 references it cites were found in a registry or an index.
Wearable Triboelectric Sensors Enabled Gait Analysis and Waist Motion Capture for IoT‐Based Smart Healthcare Applications
Open accessAbstract
Gait and waist motions always contain massive personnel information and it is feasible to extract these data via wearable electronics for identification and healthcare based on the Internet of Things (IoT). There also remains a demand to develop a cost‐effective human‐machine interface to enhance the immersion during the long‐term rehabilitation. Meanwhile, triboelectric nanogenerator (TENG) revealing its merits in both wearable electronics and IoT tends to be a possible solution. Herein, the authors present wearable TENG‐based devices for gait analysis and waist motion capture to enhance the intelligence and performance of the lower‐limb and waist rehabilitation. Four triboelectric sensors are equidistantly sewed onto a fabric belt to recognize the waist motion, enabling the real‐time robotic manipulation and virtual game for immersion‐enhanced waist training. The insole equipped with two TENG sensors is designed for walking status detection and a 98.4% identification accuracy for five different humans aiming at rehabilitation plan selection is achieved by leveraging machine learning technology to further analyze the signals. Through a lower‐limb rehabilitation robot, the authors demonstrate that the sensory system performs well in user recognition, motion monitoring, as well as robot and gaming‐aided training, showing its potential in IoT‐based smart healthcare applications.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Trung, T. Q., & Lee, N. (2016) · Advanced Materials · doi:10.1002/adma.201504244 · Cited by 2,160
Dong, K., Peng, X., & Wang, Z. L. (2020) · Advanced Materials · doi:10.1002/adma.201902549 · Cited by 1,322
Zhu, M., Sun, Z., Zhang, Z., Shi, Q., He, T., Liu, H., Chen, T., & Lee, C. (2020) · Science Advances · doi:10.1126/sciadv.aaz8693 · Cited by 784
Self-citationWearable Sensors‐Enabled Human–Machine Interaction Systems: From Design to Application
Yin, R., Wang, D., Zhao, S., Lou, Z., & Shen, G. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202008936 · Cited by 769
Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications
Jin, T., Sun, Z., Li, L., Zhang, Q., Zhu, M., Zhang, Z., Yuan, G., Chen, T., Tian, Y., Hou, X., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19059-3 · Cited by 695
Self-citationShi, Q., Dong, B., He, T., Sun, Z., Zhu, J., Zhang, Z., & Lee, C. (2020) · InfoMat · doi:10.1002/inf2.12122 · Cited by 661
Self-citationA high performance wearable strain sensor with advanced thermal management for motion monitoring
Tan, C., Dong, Z., Li, Y., Zhao, H., Huang, X., Zhou, Z., Jiang, J.-W., Long, Y.-Z., Jiang, P., Zhang, T.-Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17301-6 · Cited by 580
The Triboelectric Nanogenerator as an Innovative Technology toward Intelligent Sports
Luo, J., Gao, W., & Wang, Z. L. (2021) · Advanced Materials · doi:10.1002/adma.202004178 · Cited by 537
Self‐Powered Respiration Monitoring Enabled By a Triboelectric Nanogenerator
Su, Y., Chen, G., Chen, C., Gong, Q., Xie, G., Yao, M., Tai, H., Jiang, Y., & Chen, J. (2021) · Advanced Materials · doi:10.1002/adma.202101262 · Cited by 481
Wen, F., Sun, Z., He, T., Shi, Q., Zhu, M., Zhang, Z., Li, L., Zhang, T., & Lee, C. (2020) · Advanced Science · doi:10.1002/advs.202000261 · Cited by 478
Self-citation
Cited by
Qin, Y., Mo, J., Liu, Y., Zhang, S., Wang, J., Fu, Q., Wang, S., & Nie, S. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202201846 · Cited by 382
Cao, X., Xiong, Y., Sun, J., Xie, X., Sun, Q., & Wang, Z. L. (2023) · Nano-Micro Letters · doi:10.1007/s40820-022-00981-8 · Cited by 361
Wang, C., He, T., Zhou, H., Zhang, Z., & Lee, C. (2023) · Bioelectronic Medicine · doi:10.1186/s42234-023-00118-1 · Cited by 295
Self-citationYang, B., Cheng, J., Qu, X., Song, Y., Yang, L., Shen, J., Bai, Z., & Ji, L. (2025) · Advanced Science · doi:10.1002/advs.202408689 · Cited by 59
Wearable Healthcare and Continuous Vital Sign Monitoring with IoT Integration
Taherdoost, H. (2024) · Computers, Materials & Continua · doi:10.32604/cmc.2024.054378 · Cited by 47
Metadata
- Authors
- Zhang, Q., Jin, T., Cai, J., Xu, L., He, T., Wang, T., Tian, Y., Li, L., Peng, Y., & Lee, C.
- Venue
- Advanced Science
- Publisher
- Wiley
- Published
- 2021-11-19
- Volume
- 9
- Issue
- 4
- Pages
- e2103694-e2103694
- ISSN
- 2198-3844 (ISSN-L)
- OpenAlex
- W3214529626
- Cited by
- 386
- References
- 84
- FWCI
- 17.92
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Wearable Triboelectric Sensors Enabled Gait Analysis and Waist Motion Capture for IoT‐Based Smart Healthcare Applications. https://app.cochat.ai/papers/10.1002/advs.202103694
- The paper
- Zhang, Q., Jin, T., Cai, J., Xu, L., He, T., Wang, T., Tian, Y., Li, L., Peng, Y., & Lee, C. (2022). Wearable Triboelectric Sensors Enabled Gait Analysis and Waist Motion Capture for IoT‐Based Smart Healthcare Applications. Advanced Science. https://doi.org/10.1002/advs.202103694
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
