MXenes for wearable pressure sensing: Progress and prospects in human motion detection
Open accessAbstract
This review explores the emerging field of MXene-based pressure sensors for human motion detection. MXenes, a class of two-dimensional transition metal carbides and nitrides , offer exceptional electrical and mechanical properties ideal for wearable sensing applications. We examine the fundamental properties of MXenes, various sensing mechanisms, and innovative device architectures that have been developed. Through careful materials engineering and structural design, MXene-based pressure sensors have achieved remarkable performance metrics, including sensitivities up to 860 kPa⁻¹, ultra-low detection limits of 0.5 Pa, and rapid response times in the range of 15–40 ms. These sensors have demonstrated excellent durability, maintaining stable performance over 10,000 + loading cycles, and exhibit working ranges spanning from subtle touch pressures (∼0.5 Pa) to high pressures exceeding 200 kPa. The review covers a spectrum of applications, from large-scale body movements to subtle physiological signals, highlighting the versatility of MXene-based sensors. Recent advancements in fabrication techniques and integration methods are presented, showcasing the potential for scalable production. While significant progress has been made, challenges such as long-term stability and biocompatibility are addressed. The review concludes by outlining future research directions and potential innovations that could further enhance the capabilities of MXene-based pressure sensors in human motion detection, paving the way for advanced healthcare monitoring and human-machine interface technologies.
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Sensor units based on novel materials such as graphene, MXenes, and hydrogels achieve an excellent combination of high stretchability and high sensitivity, accurately capturing strain gradients of the epidermis during joint flexion and the trajectory of plantar pressure center migration.
“Among them, sensor units based on novel materials such as graphene 107 , MXenes 108 , and hydrogels 70 achieve an excellent combination of high stretchability and high sensitivity, accurately capturing strain gradients of the epidermis during joint flexion and the trajectory of plantar pressure center migration.”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [108]
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A novel MXene-based high-performance flexible pressure sensor for detection of human motion
Zhang, Z., Zhang, Q., Zhang, H., Li, B., Zang, J., Zhao, X., Zhao, X., & Xue, C. (2023) · Smart Materials and Structures · doi:10.1088/1361-665x/accee9 · Cited by 29 · see citing works
Applications of scaffold-based advanced materials in biomedical sensing
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On-Skin Flexible Pressure Sensor with High Sensitivity for Portable Pulse Monitoring
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Smart Detecting and Versatile Wearable Electrical Sensing Mediums for Healthcare
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A highly sensitive wearable pressure sensor capsule based on PVA/Mxene composite gel
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A Review of Epidermal Flexible Pressure Sensing Arrays
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A Flexible Piezoelectric PVDF/MXene Pressure Sensor for Roughness Discrimination
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A wearable electronic based on flexible pressure sensor for running motion monitoring
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Wireless Human Motion Detection with a Highly Sensitive Wearable Pressure Sensing Technology
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Research on high sensitivity piezoresistive sensor based on structural design
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Porous Pure MXene Fibrous Network for Highly Sensitive Pressure Sensors
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Chelation-Based Route to Aluminum-Free Layered Transition Metal Carbides (MXenes)
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Roll‐to‐Roll Manufacturing of Breathable Superhydrophobic Membranes
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Study of Aerogel-Modified Recycled Polyurethane Nanocomposites
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Race Correction and Algorithmic Bias in Atrial Fibrillation Wearable Technologies
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Let’s Print an Ecology in 3D (and 4D)
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The Effect of Reactive Sputtering on the Microstructure of Parylene-C
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Non-Intrusive Contact Respiratory Sensor for Vehicles
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A Wireless 2-Channel Layered EMG/NIRS Sensor System for Local Muscular Activity Evaluation
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Flagged first, then by citations
Cited by
Sorted by citations
Chen, S., Peng, C., Yang, B., Lin, J., Zhou, L., Jiang, Z., Liu, Z., Liu, Y., & Tang, L. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-026-00083-5 · Cited by 9 · see citing works
- Authors
- Kong, X., Fan, C., Liao, K., Zhang, W., & Xiong, H.
- Affiliations
- Yangtze University (CN)
- Venue
- Alexandria Engineering Journal
- Publisher
- Elsevier BV
- Published
- 2025-04
- Volume
- 118
- Pages
- 466-481
- ISSN
- 1110-0168 (ISSN-L)
- OpenAlex
- W4406787582
- Cited by
- 7
- References
- 137
- Citations by year
- 2026: 7
- FWCI
- 1.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Alexandria Engineering Journal (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Physical Sciences › Materials Science › Materials Chemistry › MXene and MAX Phase Materials
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- MXene, pressure sensors, wearable sensors, human motion detection, two-dimensional transition metal carbides, sensing mechanism, device architecture, mechanical durability, physiological signals, healthcare monitoring, human-machine interaction, biocompatibility, scalable fabrication
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.
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