Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Ultra-sensitive and resilient compliant strain gauges for soft machines


Soft machines are a promising design paradigm for human-centric devices1,2 and systems required to interact gently with their environment3,4. To enable soft machines to respond intelligently to their surroundings, compliant sensory feedback mechanisms are needed. Specifically, soft alternatives to strain gauges—with high resolution at low strain (less than 5 per cent)—could unlock promising new capabilities in soft systems. However, currently available sensing mechanisms typically possess either high strain sensitivity or high mechanical resilience, but not both. The scarcity of resilient and compliant ultra-sensitive sensing mechanisms has confined their operation to laboratory settings, inhibiting their widespread deployment. Here we present a versatile and compliant transduction mechanism for high-sensitivity strain detection with high mechanical resilience, based on strain-mediated contact in anisotropically resistive structures (SCARS). The mechanism relies upon changes in Ohmic contact between stiff, micro-structured, anisotropically conductive meanders encapsulated by stretchable films. The mechanism achieves high sensitivity, with gauge factors greater than 85,000, while being adaptable for use with high-strength conductors, thus producing sensors resilient to adverse loading conditions. The sensing mechanism also exhibits high linearity, as well as insensitivity to bending and twisting deformations—features that are important for soft device applications. To demonstrate the potential impact of our technology, we construct a sensor-integrated, lightweight, textile-based arm sleeve that can recognize gestures without encumbering the hand. We demonstrate predictive tracking and classification of discrete gestures and continuous hand motions via detection of small muscle movements in the arm. The sleeve demonstration shows the potential of the SCARS technology for the development of unobtrusive, wearable biomechanical feedback systems and human–computer interfaces.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it


Prices may be subject to local taxes which are calculated during checkout

Fig. 1: SCARS sensor overview.
Fig. 2: Demonstration of sensor sensitivity and robustness.
Fig. 3: The effect of off-axis loading and design parameters on sensor performance.
Fig. 4: Demonstration of a textile-based sensor-integrated sleeve for hand motion detection.

Code availability

The custom code developed for modelling sensor performance is available from

Data availability

The sensor characterization experimental data from this study are available from figshare,


  1. Sundaram, S. et al. Learning the signatures of the human grasp using a scalable tactile glove. Nature 569, 698–702 (2019).

    ADS  CAS  PubMed  Google Scholar 

  2. Kim, J. et al. Reducing the metabolic rate of walking and running with a versatile, portable exosuit. Science 365, 668–672 (2019).

    ADS  CAS  PubMed  Google Scholar 

  3. Galloway, K. C. et al. Soft robotic grippers for biological sampling on deep reefs. Soft Robot. 3, 23–33 (2016).

    PubMed  PubMed Central  Google Scholar 

  4. Xu, S. et al. Biocompatible soft fluidic strain and force sensors for wearable devices. Adv. Funct. Mater. 29, 1970038 (2019).

    Google Scholar 

  5. Truby, R. L. et al. Soft somatosensitive actuators via embedded 3D printing. Adv. Mater. 30, 1706383 (2018).

    Google Scholar 

  6. Shintake, J., Rosset, S., Schubert, B., Floreano, D. & Shea, H. Versatile soft grippers with intrinsic electroadhesion based on multifunctional polymer actuators. Adv. Mater. 28, 231–238 (2016).

    CAS  PubMed  Google Scholar 

  7. Sinatra, N. R. et al. Ultragentle manipulation of delicate structures using a soft robotic gripper. Sci. Robot. 4, eaax5425 (2019).

    PubMed  Google Scholar 

  8. Chou, H.-H. et al. A chameleon-inspired stretchable electronic skin with interactive colour changing controlled by tactile sensing. Nat. Commun. 6, 8011 (2015).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  9. Lu, N., Lu, C., Yang, S. & Rogers, J. Highly sensitive skin-mountable strain gauges based entirely on elastomers. Adv. Funct. Mater. 22, 4044–4050 (2012).

    CAS  Google Scholar 

  10. Webb, R. C. et al. Ultrathin conformal devices for precise and continuous thermal characterization of human skin. Nat. Mater. 12, 938–944 (2013).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  11. Li, L. et al. Ultrastretchable fiber sensor with high sensitivity in whole workable range for wearable electronics and implantable medicine. Adv. Sci. 5, 1800558 (2018).

    Google Scholar 

  12. Qiu, D., Chu, Y., Zeng, H., Xu, H. & Dan, G. Stretchable MoS2 electromechanical sensors with ultrahigh sensitivity and large detection range for skin-on monitoring. ACS Appl. Mater. Interfaces 11, 37035–37042 (2019).

    CAS  PubMed  Google Scholar 

  13. Gao, W. et al. Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature 529, 509–514 (2016).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  14. Panizzolo, F. A. et al. A biologically-inspired multi-joint soft exosuit that can reduce the energy cost of loaded walking. J. Neuroeng. Rehabil. 13, 43 (2016).

    PubMed  PubMed Central  Google Scholar 

  15. Araromi, O. A., Rosset, S. & Shea, H. R. High-resolution, large-area fabrication of compliant electrodes via laser ablation for robust, stretchable dielectric elastomer actuators and sensors. ACS Appl. Mater. Interfaces 7, 18046–18053 (2015).

    CAS  PubMed  Google Scholar 

  16. Sun, J.-Y., Keplinger, C., Whitesides, G. M. & Suo, Z. Ionic skin. Adv. Mater. 26, 7608–7614 (2014).

    CAS  PubMed  Google Scholar 

  17. Cotton, D. P. J., Graz, I. M. & Lacour, S. P. A multifunctional capacitive sensor for stretchable electronic skins. IEEE Sens. J. 9, 2008–2009 (2009).

    ADS  Google Scholar 

  18. Yamada, T. et al. A stretchable carbon nanotube strain sensor for human-motion detection. Nat. Nanotechnol. 6, 296–301 (2011).

    ADS  CAS  PubMed  Google Scholar 

  19. Mengüç, Y. et al. Wearable soft sensing suit for human gait measurement. Int. J. Robot. Res. 33, 1748–1764 (2014).

    Google Scholar 

  20. Seyedin, S. et al. Textile strain sensors: a review of the fabrication technologies, performance evaluation and applications. Mater. Horiz. 6, 219–249 (2019).

    CAS  Google Scholar 

  21. Kang, D. et al. Ultrasensitive mechanical crack-based sensor inspired by the spider sensory system. Nature 516, 222–226 (2014).

    ADS  CAS  PubMed  Google Scholar 

  22. Choi, Y. W. et al. Ultra-sensitive pressure sensor based on guided straight mechanical cracks. Sci. Rep. 7, 40116 (2017).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  23. Araromi, O. A., Walsh, C. J. & Wood, R. J. Hybrid carbon fiber–textile compliant force sensors for high-load sensing in soft exosuits. In 2017 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) 1798–1803 (IEEE, 2017).

  24. Wu, H., Liu, Q., Du, W., Li, C. & Shi, G. Transparent polymeric strain sensors for monitoring vital signs and beyond. ACS Appl. Mater. Interf. 10, 3895–3901 (2018).

    CAS  Google Scholar 

  25. Park, J. J., Hyun, W. J., Mun, S. C., Park, Y. T. & Park, O. O. Highly stretchable and wearable graphene strain sensors with controllable sensitivity for human motion monitoring. ACS Appl. Mater. Interf. 7, 6317–6324 (2015).

    CAS  Google Scholar 

  26. Tolvanen, J., Hannu, J. & Jantunen, H. Stretchable and washable strain sensor based on cracking structure for human motion monitoring. Sci. Rep. 8, 13241 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  27. Tolley, M. T. et al. A resilient, untethered soft robot. Soft Robot. 1, 213–223 (2014).

    Google Scholar 

  28. Kim, K. K. et al. A deep-learned skin sensor decoding the epicentral human motions. Nat. Commun. 11, 2149 (2020).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. Zhong, W. et al. Continuously producible ultrasensitive wearable strain sensor assembled with three-dimensional interpenetrating Ag nanowires/polyolefin elastomer nanofibrous composite yarn. ACS Appl. Mater. Interf. 9, 42058–42066 (2017).

    CAS  Google Scholar 

  30. Atalay, A. et al. Batch fabrication of customizable silicone-textile composite capacitive strain sensors for human motion tracking. Adv. Mater. Technol. 2, 1700136 (2017).

    Google Scholar 

  31. Lee, S. et al. A transparent bending-insensitive pressure sensor. Nat. Nanotechnol. 11, 472–478 (2016).

    ADS  CAS  PubMed  Google Scholar 

  32. Song, K. et al. Pneumatic actuator and flexible piezoelectric sensor for soft virtual reality glove system. Sci. Rep. 9, 8988 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  33. Geng, W. et al. Gesture recognition by instantaneous surface EMG images. Sci. Rep. 6, 36571 (2016).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  34. Koiva, R., Riedenklau, E., Viegas, C. & Castellini, C. Shape conformable high spatial resolution tactile bracelet for detecting hand and wrist activity. In 2015 IEEE Int. Conf. on Rehabilitation Robotics (ICORR) Vol. 2015, 157–162 (IEEE, 2015).

  35. Stefanou, T., Chance, G., Assaf, T. & Dogramadzi, S. Tactile signatures and hand motion intent recognition for wearable assistive devices. Front. Robot. AI 6, 124 (2019).

    Google Scholar 

  36. Kim, S., Jeong, D., Oh, J., Park, W. & Bae, J. A novel all-in-one manufacturing process for a soft sensor system and its application to a soft sensing glove. IEEE Int. Conf. on Intelligent Robots and Systems 7004–7009 (2018).

  37. Wininger, M., Kim, N. H. & Craelius, W. Pressure signature of forearm as predictor of grip force. J. Rehab. Res. Dev. 45, 883–892 (2008).

    Google Scholar 

  38. Ravindra, V. & Castellini, C. A comparative analysis of three non-invasive human-machine interfaces for the disabled. Front. Neurorobot. 8, 1–10 (2014).

    Google Scholar 

  39. Carpinella, I., Mazzoleni, P., Rabuffetti, M., Thorsen, R. & Ferrarin, M. Experimental protocol for the kinematic analysis of the hand: definition and repeatability. Gait Posture 23, 445–454 (2006).

    CAS  PubMed  Google Scholar 

  40. Wu, G. et al. ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion. Part II: shoulder, elbow, wrist and hand. J. Biomech. 38, 981–992 (2005).

    CAS  PubMed  Google Scholar 

  41. Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).

    CAS  PubMed  Google Scholar 

  42. Chollet, F. Keras (2015).

  43. Li, X. et al. Stretchable and highly sensitive graphene-on-polymer strain sensors. Sci. Rep. 2, 870 (2012).

    PubMed  PubMed Central  Google Scholar 

  44. Wu, J. M. et al. Ultrahigh sensitive piezotronic strain sensors based on a ZnSnO3 nanowire/microwire. ACS Nano 6, 4369–4374 (2012).

    CAS  PubMed  Google Scholar 

  45. Zhao, J. et al. Ultra-sensitive strain sensors based on piezoresistive nanographene films. Appl. Phys. Lett. 101, 063112 (2012).

    ADS  Google Scholar 

  46. Yan, C. et al. Highly stretchable piezoresistive graphene-nanocellulose nanopaper for strain sensors. Adv. Mater. 26, 2022–2027 (2014).

    CAS  PubMed  Google Scholar 

  47. Liu, Z. et al. Surface strain redistribution on structured microfibers to enhance sensitivity of fiber-shaped stretchable strain sensors. Adv. Mater. 30, 1704229 (2018).

    Google Scholar 

  48. Yin, B. et al. Highly stretchable, ultrasensitive, and wearable strain sensors based on facilely prepared reduced graphene oxide woven fabrics in an ethanol flame. ACS Appl. Mater. Interf. 9, 32054–32064 (2017).

    CAS  Google Scholar 

  49. Kim, K.-H., Jang, N.-S., Ha, S.-H., Cho, J. H. & Kim, J.-M. Highly sensitive and stretchable resistive strain sensors based on microstructured metal nanowire/elastomer composite films. Small 14, 1704232 (2018).

    Google Scholar 

  50. Wang, H. et al. Downsized sheath-core conducting fibers for weavable superelastic wires, biosensors, supercapacitors, and strain sensors. Adv. Mater. 28, 4998–5007 (2016).

    CAS  PubMed  Google Scholar 

  51. Wei, Y., Chen, S., Yuan, X., Wang, P. & Liu, L. Multiscale wrinkled microstructures for piezoresistive fibers. Adv. Funct. Mater. 26, 5078–5085 (2016).

    CAS  Google Scholar 

  52. Wu, Y.-h. et al. Liquid metal fiber composed of a tubular channel as a high-performance strain sensor. J. Mater. Chem. C 5, 12483–12491 (2017).

    CAS  Google Scholar 

  53. Jhon, Y. I. et al. Tensile characterization of single-walled carbon nanotubes with helical structural defects. Sci. Rep. 6, 20324 (2016).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  54. Shivashankar, H., Sangamesh, R. & Kulkarni, S. M. Processing and investigation of mechanical characteristics on the polydimethylsiloxane/carbon black composites. Mater. Res. Expr. 6, 105340 (2019).

    ADS  CAS  Google Scholar 

  55. Wang, S., Shan, Z. & Huang, H. The mechanical properties of nanowires. Adv. Sci. 4, 1600332 (2017).

    Google Scholar 

  56. Papageorgiou, D. G., Kinloch, I. A. & Young, R. J. Mechanical properties of graphene and graphene-based nanocomposites. Prog. Mater. Sci. 90, 75–127 (2017).

    CAS  Google Scholar 

Download references


This work was funded in part by the Tata group, the Wyss Institute for Biologically Inspired Engineering and the John A. Paulson School of Engineering and Applied Sciences at Harvard University. The prototypes presented were enabled by equipment supported by the ARO DURIP programme (award number W911NF-13-1-0311). We thank D. Wagner and D. Orzel for their assistance with the custom sleeve construction, K. Nuckols for early discussion on arm physiology, K. Becker for assistance with the experiments for sensor curvature and E. Helbling for her assistance with the sleeve system electronics and figure editing.

Author information

Authors and Affiliations



O.A.A. conceived the sensor and hardware designs. O.A.A. and A.E.P. fabricated the sensors used in the experiments. O.A.A., M.A.G., K.L.D., S.C. and R.J.W. contributed to the sensor-specific experimental design. K.L.D., M.A.G., J.J.V., O.A.A. and R.J.W. developed the analytical model. J.C.W., M.A.G. and O.A.A. contributed to sensor imaging and visualization. O.A.A. and M.A.G. conceived the arm sleeve demonstration. O.A.A., M.A.G., W.-H.H., J.R.F., C.J.W. and R.J.W. contributed to the sleeve device design and experimentation. C.J.W. and R.J.W. supervised the work. All authors contributed to the analysis, discussion of results and preparation of the manuscript.

Corresponding authors

Correspondence to Oluwaseun A. Araromi or Robert J. Wood.

Ethics declarations

Competing interests

O.A.A., C.J.W. and R.J.W. hold patents on the sensor technology.

Additional information

Peer review information Nature thanks Tse Nga Ng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 CFPC fabrication and characterization.

a, Five plies of uniaxially aligned carbon fibres pre-impregnated with epoxy resin are laid in a [0°/90°/0°/90°/0°] layup. (Fluorinated ethylene propylene (FEP) films were used to prevent adhesion between the steel plates and the CFPC plies.) The stack is cured in a heat press using the pressure and heat profile indicated in b. c, CFPC electromechanical response (sample gauge length taken to be 10 mm). Inset, test sample geometry and carbon fibre ply orientations relative to the test pull direction. d, The conductor toughness (estimated by multiplying the ultimate yield stress by the strain at failure divided by two) weighted (multiplied) by the smallest conductor dimension, dmin (that is, the conductor thickness or diameter), plotted against the linear gauge factor. Data are from refs. 9,11,16,18,21,22,25,26,29,43,44,45,47,48,49,50. (Where the linear gauge factor was not available, the absolute gauge factor was used. Where the yield stress was not stated, generally accepted values or data from references. 53,54,55,56 were used. Where ambiguities existed, the higher value estimate was used.) e, Sensor with the highest gauge factor obtained in this work showing a gauge factor of 85,000 and a linearity, R2 > 0.96. The inset shows a layup of the conductor used in the experiment, showing an insulating epoxy laminate (FR4) sandwiched on each side by two aligned CFPC plies.

Extended Data Fig. 2 Sensor fabrication and resistance measurement.

a, Sensor laser-machining and assembly steps. b, Laser processing file and resistance measurement circuit. A current regulator (LM134 by Texas Instruments) provides a constant current, I to the sensor. The potential difference, V is measured and read using a digital acquisition system. The bottom left schematic shows a close up of the region indicated in the top figure (red box). The bottom right schematic shows a microscope image of an example CFPC meander (scale bar, 100 µm). c, Example of sensor cross-section (seen from the edge of the meander, CFPC trace outlined in red). The top image shows the cross-section before prestrain relaxation and the bottom image shows the cross-section after prestrain release with trace contact (scale bars, 50 µm). d, Sensor electrical connection methodology. (i) Contact pad of the sensor. (ii) Stripped electrical cable (34-gauge, 7/40 Hook-Up Wire by Alpha Wire) passed through the hole of the contact pad. (iii) The cable is pulled through the slot feature in the contact pad. (iv) The resulting electrical connection, providing good conductivity and mechanical stability.

Extended Data Fig. 3 Sensor resilience to adverse conditions, off-axis deformations, strain rate and textile coupling.

a, Photograph of the car used in the sensor resilience demonstration (Honda HR-V Henna), mass approximately 1,450 kg. b, Experimental setup for demonstration of robustness. The inset shows a close-up of the sensor with wiring and adhesive covering to prevent wire breakage (scale bar, 20 mm). c, Results of high loads (applied normal to the sensor surface, as indicated in the inset above) on the sensor transient. The sensor is functional throughout the test and qualitative behaviour is retained. d, The effect of temperature and humidity on sensor output (the inset shows a diagram of the sensor during the experiment). e, The effect of strain rate on the sensor transient (arrows indicate loading and unloading portions of the curve). f, The effect of coupling a sensor in series with a relatively stiff textile (knitted textile, stiffness 7.5 × 10−2 N mm−1) and a relatively low stiffness textile (mesh textile, 2 × 10−2 N mm−1). The inset shows the sensor coupling to textiles of length 100 mm, in addition to a sensor with no textile coupling (provided for comparison) (the coloured dots indicate which plot line corresponds to which sensor in the inset). g, Plot showing low sensitivity of the sensor response to off-axis deformations about non-principal axes. The insets show screen captures of experimental footage showing deformations applied to the sensor.

Extended Data Fig. 4 Experimental setup for initial curvature and twist characterizations.

a, The characterization of initial torsion (and linear extension). b, The response to normal pressure. c, The characterization of initial curvature, where d represents the diameter of the PVC pipe used in the experiment. The inset shows the geometric relationship between the measured force, Fm and the force in the flexible connector, F (equal to Fmcosθ).

Extended Data Fig. 5 Sensor analytical model.

a, A schematic depiction of the simplified sensor geometry used to develop the analytical model and the resistive ladder electrical abstraction. The lumped elements labelled RC (for resistive contact) represent the contact-dependent variable resistance. bd, Sensor model prediction for: b, sensor gauge factor; c, tensile strain in the sensing region; and d, resistance change, ∆R/R0 (all normalized by their values at scaling factor 1), as a function of varying width, pitch and anisotropy ratio (AR) (in c, the width line is overlapped by the AR line). A scaling factor of 1 is equivalent to width 10 mm, pitch 200 µm and AR = 1.2. All other parameters are held constant. The figure shows that for the range of values explored, the gauge factor and resistance range are most sensitive to variations in the sensor width, and the sensor strain range is most sensitive to variations in the sensor pitch.

Extended Data Fig. 6 Sensor sleeve design and testing.

a, Sleeve pattern dimensions. b, Sensor sleeve device, front side view. c, Sensor sleeve device, back side view. Textile-integrated sensors, zip fasteners, elbow alignment feature and sewn coaxial cable are labelled (the sewn coaxial cable method allows for the textile region underlying the cable to be stretched without breaking the cable.) df, Motion tracking marker locations for motion capture data acquisitions. d, View from ventral side of the arm. e, Frontal plane view of the arm. f, Dorsal side view of the arm. Example of motion capture tracking for: g, palm gesture; h, pinch gesture; i, fist gesture. In gi, left image: screen capture of footage from the experiment, right image: screen capture of the motion tracking reconstruction, coloured dots represent the motion tracking markers (yellow connecting rods added for clarity). Author M.A.G. appears in di; author J.R.F. appears in f. Tracking images were generated using Visual3D software, C-Motion by Germantown.

Extended Data Fig. 7 Taxonomy of compliant, resistive sensing mechanisms.

Red crosses in the strain response row indicate the point at which the sensor typically fails (the strain value at failure is indicated in blue text). The ultra-sensitivity determination is based on the linear gauge factor of the sensor (as opposed to the absolute gauge factor; see Extended Data Table 3). For each of the four categories at the bottom of the table, a green check indicates that ultra-sensitivity (linear gauge factors >1,000) has been demonstrated, off-axis deformation rejection has been shown, the ability to withstand cyclic loading >500 cycles has been demonstrated, and the conductors used have high resilience (as defined in Extended Data Fig. 5d), respectively. A red cross indicates where the above criteria have not been met. Data are from this work and refs. 9,11,18,21,22,25,26,29,43,44,45,46,47,48,49,52.

Extended Data Table 1 Motion capture trial protocol
Extended Data Table 2 Selected hand motion tracking and classification model architectures
Extended Data Table 3 Comparison table of compliant strain sensor mechanisms

Supplementary information

Video 1

Demonstration of sensor mechanical resilience: The SCARS sensor technology is subjected to a number of challenging scenarios for robustness and damage resistance; including puncturing with a scalpel knife, impact loading from a hammer strike, and even extremely adverse conditions outside of the laboratory context – being driven over by a car on uneven terrain.

Video 2

Comparison of the sensor response to linear extension vs bending: A sensor is cyclically and sequentially bent and extended. These results show that the peak response in bending is less than 10% of the peak response in linear extension for bend angles of up to 80˚.

Video 3

Demonstration of sensor sleeve device hand gesture classification and hand motion tracking: An example soft device is presented through the construction of a textile-based sensor sleeve – designed to recognize hand motions and gestures via the detection of subtle deformations in the arm resulting from muscle contraction.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Araromi, O.A., Graule, M.A., Dorsey, K.L. et al. Ultra-sensitive and resilient compliant strain gauges for soft machines. Nature 587, 219–224 (2020).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing