With the rapid development of deep learning and computing power, human–computer interactions, and interfaces are attracting attentions in industrial and academic research. Flexible human–computer interaction can greatly improve productivity and enable robots to work in extreme environments that humans cannot tolerate. The research of gesture recognition is emerging and provides a new way of studying the human–computer interactions. However, compared with the entire human body, human hands are dexterous organs with more complex and flexible joints, which makes hand gesture recognition a challenging problem. Here, a robust and cost‐effective gesture recognition system is reported through the soft optoelectronic sensors. An array of polymer‐encapsulated U‐shaped microfiber (UMF) attached to a glove is fabricated for sensitive finger motion detection. The anisotropic strain response of UMF is measured with a sensitivity of 15.98 (2.20) in the x‐direction (y‐direction). A deep learning network (VGGNet) is developed to process the optical signals for analyzing and classifying hand gestures. The experiments show that VGGNet has high recognition accuracy of 99.2% for the test datasets with ten classified gestures. This work provides a potential optical interface in studying gesture recognition and biomechanical signatures, which can also be applied in virtual reality systems and interactive game platforms.

Miniaturized spectrometers have been widely researched in recent years, but few studies are conducted with on-chip multimode schemes for mode-division multiplexing (MDM) systems. Here we propose an ultracompact mode-division demultiplexing spectrometer that includes branched waveguide structures and graphene-based photodetectors, which realizes simultaneously spectral dispersing and light fields detecting. In the bandwidth of 1500-1600 nm, the designed spectrometer achieves the single-mode spectral resolution of 7 nm for each mode of TE 1 -TE 4 by Tikhonov regularization optimization. Empowered by deep learning algorithms, the 15-nm resolution of parallel reconstruction for TE 1 -TE 4 is achieved by a single-shot measurement. Moreover, by stacking the multimode response in TE 1 -TE 4 to the single spectra, the 3-nm spectral resolution is realized. This design reveals an effective solution for on-chip MDM spectroscopy, and may find applications in multimode sensing, interconnecting and processing.

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The complete description of a continuous-wave light field includes its four fundamental properties: wavelength, polarization, phase and amplitude. However, the simultaneous measurement of a multi-dimensional light field of such four degrees of freedom is challenging in conventional optical systems requiring a cascade of dispersive and polarization elements. In this work, we demonstrate a disordered-photonics-assisted intelligent four-dimensional light field sensor. This is achieved by discovering that the speckle patterns, generated from light scattering in a disordered medium, are intrinsically sensitive to a high-dimension light field given their high structural degrees of freedom. Further, the multi-task-learning deep neural network is leveraged to process the single-shot light-field-encoded speckle images free from any prior knowledge of the complex disordered structures and realizes the high-accuracy recognition of full-Stokes vector, multiple orbital angular momentum (OAM), wavelength and power. The proof-of-concept study shows that the states space of four-dimensional light field spanning as high as 1680=4 (multiple-OAM) $$\times$$ × 2 (OAM power spectra) $$\times$$ × 15 (multiple-wavelength) $$\times$$ × 14 (polarizations) can be well recognized with high accuracy in the chip-integrated sensor. Our work provides a novel paradigm for the design of optical sensors for high-dimension light fields, which can be widely applied in optical communication, holography, and imaging.