Real-time multiaxial strain mapping using computer vision integrated optical sensors
Source abstract
Abstract
Abstract Soft strain sensors pose great potential for emerging human–machine interfaces. However, their real-world applications have been limited due to challenges such as low reproducibility, susceptibility to environmental noise, and short lifetimes, which are attributed to nanotechnologies, including microfabrication techniques. In this study, we present a computer vision-based optical strain (CVOS) sensor system that integrates computer vision with streamlined microfabrication techniques to overcome these challenges and facilitate real-time multiaxial strain mapping. The proposed CVOS sensor consists of an easily fabricated soft silicone substrate with micro-markers and a tiny camera for highly sensitive marker detection. Real-time multiaxial strain mapping allows for measuring and distinguishing complex multi-directional strain patterns, providing the proposed CVOS sensor with higher scalability. Our results indicate that the proposed CVOS sensor is a promising approach for the development of highly sensitive and versatile human–machine interfaces that can operate long-term under real-world conditions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Tactile and Sensory Interactions · Advanced Fiber Optic Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (52)
Show source-provided references
1. Yamada, T. et al. A stretchable carbon nanotube strain sensor for human-motion detection. Nat. Nanotechnol. 6, 296–301 (2011).10.1038/nnano.2011.36 ↗
2. Qiu, Y. et al. Nondestructive identification of softness via bioinspired multisensory electronic skins integrated on a robotic hand. npj Flex Electron. 6, 1–10 (2022).10.1038/s41528-022-00181-9 ↗
3. Yin, R., Wang, D., Zhao, S., Lou, Z. & Shen, G. Wearable sensors-enabled human–machine interaction systems: from design to application. Adv. Funct. Mater. 31, 2008936 (2021).10.1002/adfm.202008936 ↗
4. Jin, T. et al. Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications. Nat. Commun. 11, 5381 (2020).10.1038/s41467-020-19059-3 ↗
5. Chu, M. et al. Respiration rate and volume measurements using wearable strain sensors. npj Digit. Med. 2, 8 (2019).10.1038/s41746-019-0083-3 ↗
6. Dinh, T. et al. Stretchable respiration sensors: advanced designs and multifunctional platforms for wearable physiological monitoring. Biosens. Bioelectron. 166, 112460 (2020).10.1016/j.bios.2020.112460 ↗
7. Wang, R. et al. Cost-effective fabrication of transparent strain sensors via micro-scale 3D printing and imprinting. Nanomaterials (Basel) 12, 120 (2021).10.3390/nano12010120 ↗
8. Park, S. J., Kim, J., Chu, M. & Khine, M. Flexible piezoresistive pressure sensor using wrinkled carbon nanotube thin films for human physiological signals. Adv. Mater. Technol. 3, 1700158 (2018).10.1002/admt.201700158 ↗
9. Wang, Y. et al. Hierarchically buckled Ti3C2Tx MXene/carbon nanotubes strain sensor with improved linearity, sensitivity, and strain range for soft robotics and epidermal monitoring. Sens. Actuators B 368, 132228 (2022).10.1016/j.snb.2022.132228 ↗
10. Ko, Y., Kim, J. S., Vu, C. C. & Kim, J. Ultrasensitive strain sensor based on pre-generated crack networks using Ag nanoparticles/single-walled carbon nanotube (SWCNT) hybrid fillers and a polyester woven elastic band. Sensors 21, 2531 (2021).10.3390/s21072531 ↗
11. Kim, K. H. et al. Enhancement of linearity range of stretchable ultrasensitive metal crack strain sensor: Via superaligned carbon nanotube-based strain engineering. Mater. Horiz. 7, 2662–2672 (2020).10.1039/D0MH00806K ↗
12. Nag, A., Alahi, M. E. E., Mukhopadhyay, S. C. & Liu, Z. Multi-walled carbon nanotubes-based sensors for strain sensing applications. Sensors (Basel) 21, 1261 (2021).10.3390/s21041261 ↗
13. Choi, Y. K. et al. Wearable anti-temperature interference strain sensor with metal nanoparticle thin film and hybrid ligand exchange. Nanoscale 14, 8628–8639 (2022).10.1039/D2NR02392J ↗
14. Zhang, W., Liu, Q. & Chen, P. Flexible strain sensor based on carbon black/silver nanoparticles composite for humanmotion detection. Materials (Basel) 11, 1836 (2018).10.3390/ma11101836 ↗
15. Cheng, H. W., Yan, S., Shang, G., Wang, S. & Zhong, C. J. Strain sensors fabricated by surface assembly of nanoparticles. Biosens. Bioelectron. 186, 113268 (2021).10.1016/j.bios.2021.113268 ↗
16. Luan, J., Wang, Q., Zheng, X., Li, Y. & Wang, N. Flexible metal/polymer composite films embedded with silver nanowires as a stretchable and conductive strain sensor for human motion monitoring. Micromachines (Basel) 10, 372 (2019).10.3390/mi10060372 ↗
17. Gong, S. et al. A wearable and highly sensitive pressure sensor with ultrathin gold nanowires. Nat. Commun. 5, 3132 (2014).10.1038/ncomms4132 ↗
18. Kumar, K. S. et al. Stretchable and sensitive silver nanowire-hydrogel strain sensors for proprioceptive actuation. Acs. Appl. Mater. Interfaces 13, 37816–37829 (2021).10.1021/acsami.1c08305 ↗
19. Jung, H. et al. Nano-cracked strain sensor with high sensitivity and linearity by controlling the crack arrangement. Sensors (Basel) 19, 2834 (2019).10.3390/s19122834 ↗
20. Park, C. et al. One-step laser encapsulation of nano-cracking strain sensors. Sensors (Basel) 18, 2673 (2018).10.3390/s18082673 ↗
21. Jeon, H., Hong, S. K., Kim, M. S., Cho, S. J. & Lim, G. Omni-purpose stretchable strain sensor based on a highly dense nanocracking structure for whole-body motion monitoring. ACS Appl. Mater. Interfaces 9, 41712–41721 (2017).10.1021/acsami.7b14153 ↗
22. Kang, S., Rachim, V. P., Baek, J. H., Lee, S. Y. & Park, S. M. A flexible patch-type strain sensor based on polyaniline for continuous monitoring of pulse waves. IEEE Access 8, 152105–152115 (2020).10.1109/ACCESS.2020.3017218 ↗
23. Rachim, V. P., Kang, S., Baek, J. H. & Park, S. M. Unobtrusive, cuffless blood pressure monitoring using a soft polymer sensor array with flexible hybrid electronics. IEEE Sens. J. 21, 10132–10142 (2021).10.1109/JSEN.2021.3059864 ↗
24. Wang, W. et al. Fingerprint-inspired strain sensor with balanced sensitivity and strain range using laser-induced graphene. ACS Appl. Mater. Interfaces 14, 1315–1325 (2022).10.1021/acsami.1c16646 ↗
25. Xia, S. Y. et al. Laser-induced graphene (LIG)-based pressure sensor and triboelectric nanogenerator towards high-performance self-powered measurement-control combined system. Nano Energy. 96, 107099 (2022).10.1016/j.nanoen.2022.107099 ↗
26. Jeong, S. Y. et al. Highly skin-conformal laser-induced graphene-based human motion monitoring sensor. Nanomaterials (Basel) 11, 951 (2021).10.3390/nano11040951 ↗
27. Rachim, V. P. et al. A scalable laser-centric fabrication of an epidermal cardiopulmonary patch. Adv. Mater. Technol. 7, 2200242 (2022).10.1002/admt.202200242 ↗
28. Gu, J. et al. Self-powered strain sensor based on the piezo-transmittance of a mechanical metamaterial. Nano Energy 89, 106447 (2021).10.1016/j.nanoen.2021.106447 ↗
29. Naruse, M. et al. Randomness in highly reflective silver nanoparticles and their localized optical fields. Sci. Rep. 4, 6077 (2014).10.1038/srep06077 ↗
30. Zebarjadi, M., Esfarjani, K., Bian, Z. & Shakouri, A. Low-temperature thermoelectric power factor enhancement by controlling nanoparticle size distribution. Nano. Lett. 11, 225–230 (2011).10.1021/nl103581z ↗
31. Li, Y. et al. Low‐Cost Data Glove based on deep‐learning‐enhanced flexible multiwalled carbon nanotube sensors for real‐time gesture recognition. Adv. Intell. Syst. 4, 2200128 (2022).10.1002/aisy.202200128 ↗
32. Marin, B. C. et al. Graphene-metal composite sensors with near-zero temperature coefficient of resistance. ACS Omega 2, 626–630 (2017).10.1021/acsomega.7b00044 ↗
33. Lee, S., Kim, E. M. & Lim, Y. Near-zero temperature coefficient of resistance of hybrid resistor fabricated with carbon nanotube and metal alloy. Sci. Rep. 9, 7763 (2019).10.1038/s41598-019-44182-7 ↗
34. Kwon, Y. et al. Effects of bending strain and crack direction on crack-based strain sensors. Smart. Mater. Struct. 29, 115007 (2020).10.1088/1361-665X/ab9f19 ↗
35. Kanao, K. et al. Highly selective flexible tactile strain and temperature sensors against substrate bending for an artificial skin. RSC Adv. 5, 30170–30174 (2015).10.1039/C5RA03110A ↗
36. Xu, X. L., Li, S. X., Yang, Y., Sun, X. C. & Xia, H. High-performance strain sensor for detection of human motion and subtle strain by facile fabrication. Measurement 189, 110658 (2022).10.1016/j.measurement.2021.110658 ↗
37. Zhang, S. et al. Highly stretchable, sensitive, and flexible strain sensors based on silver nanoparticles/carbon nanotubes composites. J. Alloy. Compd. 652, 48–54 (2015).10.1016/j.jallcom.2015.08.187 ↗
38. Wang, Z., Cong, Y. & Fu, J. Stretchable and tough conductive hydrogels for flexible pressure and strain sensors. J. Mater. Chem. B. 8, 3437–3459 (2020).10.1039/C9TB02570G ↗
39. Zhang, W. et al. Anti-freezing, water-retaining, conductive, and strain-sensitive hemicellulose/polypyrrole composite hydrogels for flexible sensors. J. Mater. Res. Technol. 14, 555–566 (2021).10.1016/j.jmrt.2021.06.088 ↗
40. Gu, J., Kwon, D., Ahn, J. & Park, I. Wearable strain sensors using light transmittance change of carbon nanotube-embedded elastomers with microcracks. Acs. Appl. Mater. Interfaces 12, 10908–10917 (2020).10.1021/acsami.9b18069 ↗
41. Zhai, Q. & Yang, Y. Highly stretchable variable-transmittance skin for ultrasensitive and wearable strain sensing. Adv. Mater. Technol. 2, 1700161 (2017).10.1002/admt.201700161 ↗
42. Zuo, C. et al. Deep learning in optical metrology: a review. Light. Sci. Appl. 11, 1–54 (2022).10.1038/s41377-021-00680-w ↗
43. Genty, G. et al. Machine learning and applications in ultrafast photonics. Nat. Photonics 15, 91–101 (2021).10.1038/s41566-020-00716-4 ↗
44. Xu, S. et al. Computer vision techniques in construction: a critical review. Arch. Comput. Methods Eng. 28, 3383–3397 (2021).10.1007/s11831-020-09504-3 ↗
45. Aurenhammer, F. Voronoi Diagrams—a survey of a fundamental geometric data structure. ACM Comput. Surv. 23, 345–405 (1991).10.1145/116873.116880 ↗
46. Liu, K. C., Hsieh, C. Y., Hsu, S. J. P. & Chan, C. T. Impact of sampling rate on wearable-based fall detection systems based on machine learning models. IEEE Sens. J. 18, 9882–9890 (2018).10.1109/JSEN.2018.2872835 ↗
47. Zhang, S. et al. Activity classification using the Genea: Optimum sampling frequency and number of axes. Med. Sci. Sports Exerc. 44, 2228–2234 (2012).10.1249/MSS.0b013e31825e19fd ↗
48. Faisal, A. I. et al. Monitoring methods of human body joints: state-of-the-art and research challenges. Sensors (Basel) 19, 2629 (2019).10.3390/s19112629 ↗
49. Lee, J. H., Chee, P. S., Lim, E. H., Low, J. H. & Nguyen, N. T. A stretchable kirigami-inspired self-powered electroactive sensor for tensile strain and torsion sensing. Adv. Eng. Mater. 24, 2100961 (2022).10.1002/adem.202100961 ↗
50. Lee, M., Lee, S. & Lim, S. Electromagnetic control by actuating kirigami-inspired shape memory alloy: thermally reconfigurable antenna application. Sensors (Basel) 21, 3026 (2021).10.3390/s21093026 ↗
51. Pizer, S. M. et al. Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39, 355–368 (1987).10.1016/S0734-189X(87)80186-X ↗
52. Otsu, N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man. Cybern. 9, 62–66 (1979).10.1109/TSMC.1979.4310076 ↗