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Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors


Gesture recognition using machine-learning methods is valuable in the development of advanced cybernetics, robotics and healthcare systems, and typically relies on images or videos. To improve recognition accuracy, such visual data can be combined with data from other sensors, but this approach, which is termed data fusion, is limited by the quality of the sensor data and the incompatibility of the datasets. Here, we report a bioinspired data fusion architecture that can perform human gesture recognition by integrating visual data with somatosensory data from skin-like stretchable strain sensors made from single-walled carbon nanotubes. The learning architecture uses a convolutional neural network for visual processing and then implements a sparse neural network for sensor data fusion and recognition at the feature level. Our approach can achieve a recognition accuracy of 100% and maintain recognition accuracy in non-ideal conditions where images are noisy and under- or over-exposed. We also show that our architecture can be used for robot navigation via hand gestures, with an error of 1.7% under normal illumination and 3.3% in the dark.

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Fig. 1: Bioinspired somatosensory–visual associated learning framework.
Fig. 2: Characterization of stretchable strain sensor.
Fig. 3: Dataset preparation for BSV associated learning.
Fig. 4: BSV associated learning for classification.
Fig. 5: Precise HGR based on BSV for human–machine interaction.

Data availability

The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request. The SV datasets used in this study are available at

Code availability

The code that supports the plots within this paper and other findings of this study are available at The code that supports the human–machine interaction experiment is available from the corresponding author upon reasonable request.


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The project was supported by the Agency for Science, Technology and Research (A*STAR) under its Advanced Manufacturing and Engineering (AME) Programmatic Scheme (no. A18A1b0045), the National Research Foundation (NRF), Prime Minister’s office, Singapore, under its NRF Investigatorship (NRF-NRFI2017-07), Singapore Ministry of Education (MOE2017-T2-2-107) and the Australian Research Council (ARC) under Discovery Grant DP200100700. We thank all the volunteers for collecting data and also A.L. Chun for critical reading and editing of the manuscript.

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Authors and Affiliations



M.W. and X.C. designed the study. M.W. designed and characterized the strain sensor. M.W., T.W. and P.C. fabricated the PAA hydrogels. Z.Y. and M.W. carried out the machine learning algorithms and analysed the results. M.W., S.G. and Y.Z. collected the SV data. M.W. performed the human–machine interaction experiment. M.W. and X.C. wrote the paper and all authors provided feedback.

Corresponding author

Correspondence to Xiaodong Chen.

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Supplementary information

Supplementary Information

Supplementary Notes 1–3, Figs. 1–11, Tables 1–3 and refs. 1–7.

Reporting Summary

Supplementary Video 1

Conformable and adhesive stretchable strain sensor.

Supplementary Video 2

Comparison of the robot navigation using the BSV learning-based and visual-based hand gesture recognition under a normal illuminance of 431 lux.

Supplementary Video 3

Comparison of the robot navigation using the BSV learning-based and visual-based hand gesture recognition under a dark illuminance of 10 lux.

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Wang, M., Yan, Z., Wang, T. et al. Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors. Nat Electron 3, 563–570 (2020).

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