Flexible electrical impedance tomography for tactile interfaces

Flexible electrical impedance tomography (EIT) is an emerging technology for tactile sensing in human-machine interfaces (HMI). It offers a unique alternative to traditional array-based tactile sensors with its flexible, scalable, and cost-effective one-piece design. This paper proposes a lattice-patterned flexible EIT tactile sensor with a hydrogel-based conductive layer, designed for enhanced sensitivity while maintaining durability. We conducted simulation studies to explore the influence of lattice width and conductive layer thickness on sensor performance, establishing optimized sensor design parameters for enhanced functionality. Experimental evaluations demonstrate the sensor's capacity to detect diverse tactile patterns with a high accuracy. The practical utility of the sensor is demonstrated through its integration within an HMI setup to control a virtual game, showcasing its potential for dynamic, multi-functional tactile interactions in real-time applications. This study reinforces the potential of EIT-based flexible tactile sensors, establishing a foundation for future advancements in wearable, adaptable HMI technologies.

Data-efficient Tactile Sensing with Electrical Impedance Tomography

Electrical Impedance Tomography (EIT)-inspired tactile sensors are gaining attention in robotic tactile sensing due to their cost-effectiveness, safety, and scalability with sparse electrode configurations. This paper presents a data augmentation strategy for learning-based tactile reconstruction that amplifies the original single-frame signal measurement into 32 distinct, effective signal data for training. This approach supplements uncollected conditions of position information, resulting in more accurate and high-resolution tactile reconstructions. Data augmentation for EIT significantly reduces the required EIT measurements and achieves promising performance with even limited samples. Simulation results show that the proposed method improves the correlation coefficient by over 12% and reduces the relative error by over 21% under various noise levels. Furthermore, we demonstrate that a standard deep neural network (DNN) utilizing the proposed data augmentation reduces the required data down to 1/31 while achieving a similar tactile reconstruction quality. Real-world tests further validate the approach's effectiveness on a flexible EIT-based tactile sensor. These results could help address the challenge of training tactile sensing networks with limited available measurements, improving the accuracy and applicability of EIT-based tactile sensing systems.

Multi-frequency Electrical Impedance Tomography Reconstruction with Multi-Branch Attention Image Prior

Multi-frequency Electrical Impedance Tomography (mfEIT) is a promising biomedical imaging technique that estimates tissue conductivities across different frequencies. Current state-of-the-art (SOTA) algorithms, which rely on supervised learning and Multiple Measurement Vectors (MMV), require extensive training data, making them time-consuming, costly, and less practical for widespread applications. Moreover, the dependency on training data in supervised MMV methods can introduce erroneous conductivity contrasts across frequencies, posing significant concerns in biomedical applications. To address these challenges, we propose a novel unsupervised learning approach based on Multi-Branch Attention Image Prior (MAIP) for mfEIT reconstruction. Our method employs a carefully designed Multi-Branch Attention Network (MBA-Net) to represent multiple frequency-dependent conductivity images and simultaneously reconstructs mfEIT images by iteratively updating its parameters. By leveraging the implicit regularization capability of the MBA-Net, our algorithm can capture significant inter- and intra-frequency correlations, enabling robust mfEIT reconstruction without the need for training data. Through simulation and real-world experiments, our approach demonstrates performance comparable to, or better than, SOTA algorithms while exhibiting superior generalization capability. These results suggest that the MAIP-based method can be used to improve the reliability and applicability of mfEIT in various settings.

Closed-loop underwater soft robotic foil shape control using flexible e-skin

The use of soft robotics for real-world underwater applications is limited, even more than in terrestrial applications, by the ability to accurately measure and control the deformation of the soft materials in real time without the need for feedback from an external sensor. Real-time underwater shape estimation would allow for accurate closed-loop control of soft propulsors, enabling high-performance swimming and manoeuvring. We propose and demonstrate a method for closed-loop underwater soft robotic foil control based on a flexible capacitive e-skin and machine learning which does not necessitate feedback from an external sensor. The underwater e-skin is applied to a highly flexible foil undergoing deformations from 2% to 9% of its camber by means of soft hydraulic actuators. Accurate set point regulation of the camber is successfully tracked during sinusoidal and triangle actuation routines with an amplitude of 5% peak-to-peak and 10-second period with a normalised RMS error of 0.11, and 2% peak-to-peak amplitude with a period of 5 seconds with a normalised RMS error of 0.03. The tail tip deflection can be measured across a 30 mm (0.15 chords) range. These results pave the way for using e-skin technology for underwater soft robotic closed-loop control applications.

Generative deep learning-enabled ultra-large field-of-view lens-free imaging

Advancements in high-throughput biomedical applications necessitate real-time, large field-of-view (FOV) imaging capabilities. Conventional lens-free imaging (LFI) systems, while addressing the limitations of physical lenses, have been constrained by dynamic, hard-to-model optical fields, resulting in a limited one-shot FOV of approximately 20 $mm^2$. This restriction has been a major bottleneck in applications like live-cell imaging and automation of microfluidic systems for biomedical research. Here, we present a deep-learning(DL)-based imaging framework - GenLFI - leveraging generative artificial intelligence (AI) for holographic image reconstruction. We demonstrate that GenLFI can achieve a real-time FOV over 550 $mm^2$, surpassing the current LFI system by more than 20-fold, and even larger than the world's largest confocal microscope by 1.76 times. The resolution is at the sub-pixel level of 5.52 $\mu m$, without the need for a shifting light source. The unsupervised learning-based reconstruction does not require optical field modeling, making imaging dynamic 3D samples (e.g., droplet-based microfluidics and 3D cell models) in complex optical fields possible. This GenLFI framework unlocks the potential of LFI systems, offering a robust tool to tackle new frontiers in high-throughput biomedical applications such as drug discovery.

Digital twin-assisted three-dimensional electrical capacitance tomography for multiphase flow imaging

Three-dimensional electrical capacitance tomography (3D-ECT) has shown promise for visualizing industrial multiphase flows. However, existing 3D-ECT approaches suffer from limited imaging resolution and lack assessment metrics, hampering their effectiveness in quantitative multiphase flow imaging. This paper presents a digital twin (DT)-assisted 3D-ECT, aiming to overcome these limitations and enhance our understanding of multiphase flow dynamics. The DT framework incorporates a 3D fluid-electrostatic field coupling model (3D-FECM) that digitally represents the physical 3D-ECT system, which enables us to simulate real multiphase flows and generate a comprehensive virtual multiphase flow 3D imaging dataset. Additionally, the framework includes a deep neural network named 3D deep back projection (3D-DBP), which learns multiphase flow features from the virtual dataset and enables more accurate 3D flow imaging in the real world. The DT-assisted 3D-ECT was validated through virtual and physical experiments, demonstrating superior image quality, noise robustness and computational efficiency compared to conventional 3D-ECT approaches. This research contributes to developing accurate and reliable 3D-ECT techniques and their implementation in multiphase flow systems across various industries.

Touch and deformation perception of soft manipulators with capacitive e-skins and deep learning

Tactile sensing in soft robots remains particularly challenging because of the coupling between contact and deformation information which the sensor is subject to during actuation and interaction with the environment. This often results in severe interference and makes disentangling tactile sensing and geometric deformation difficult. To address this problem, this paper proposes a soft capacitive e-skin with a sparse electrode distribution and deep learning for information decoupling. Our approach successfully separates tactile sensing from geometric deformation, enabling touch recognition on a soft pneumatic actuator subject to both internal (actuation) and external (manual handling) forces. Using a multi-layer perceptron, the proposed e-skin achieves 99.88\% accuracy in touch recognition across a range of deformations. When complemented with prior knowledge, a transformer-based architecture effectively tracks the deformation of the soft actuator. The average distance error in positional reconstruction of the manipulator is as low as 2.905$\pm$2.207 mm, even under operative conditions with different inflation states and physical contacts which lead to additional signal variations and consequently interfere with deformation tracking. These findings represent a tangible way forward in the development of e-skins that can endow soft robots with proprioception and exteroception.

Regularized Shallow Image Prior for Electrical Impedance Tomography

Untrained Neural Network Prior (UNNP) based algorithms have gained increasing popularity in tomographic imaging, as they offer superior performance compared to hand-crafted priors and do not require training. UNNP-based methods usually rely on deep architectures which are known for their excellent feature extraction ability compared to shallow ones. Contrary to common UNNP-based approaches, we propose a regularized shallow image prior method that combines UNNP with hand-crafted prior for Electrical Impedance Tomography (EIT). Our approach employs a 3-layer Multi-Layer Perceptron (MLP) as the UNNP in regularizing 2D and 3D EIT inversion. We demonstrate the influence of two typical hand-crafted regularizations when representing the conductivity distribution with shallow MLPs. We show considerably improved EIT image quality compared to conventional regularization algorithms, especially in structure preservation. The results suggest that combining the shallow image prior and the hand-crafted regularization can achieve similar performance to the Deep Image Prior (DIP) but with less architectural dependency and complexity of the neural network.

A Learning-Based 3D EIT Image Reconstruction Method

Deep learning has been widely employed to solve the Electrical Impedance Tomography (EIT) image reconstruction problem. Most existing physical model-based and learning-based approaches focus on 2D EIT image reconstruction. However, when they are directly extended to the 3D domain, the reconstruction performance in terms of image quality and noise robustness is hardly guaranteed mainly due to the significant increase in dimensionality. This paper presents a learning-based approach for 3D EIT image reconstruction, which is named Transposed convolution with Neurons Network (TN-Net). Simulation and experimental results show the superior performance and generalization ability of TN-Net compared with prevailing 3D EIT image reconstruction algorithms.

Impedance-optical Dual-modal Cell Culture Imaging with Learning-based Information Fusion

While Electrical Impedance Tomography (EIT) has found many biomedicine applications, a better resolution is needed to provide quantitative analysis for tissue engineering and regenerative medicine. This paper proposes an impedance-optical dual-modal imaging framework, which is mainly aimed at high-quality 3D cell culture imaging and can be extended to other tissue engineering applications. The framework comprises three components, i.e., an impedance-optical dual-modal sensor, the guidance image processing algorithm, and a deep learning model named multi-scale feature cross fusion network (MSFCF-Net) for information fusion. The MSFCF-Net has two inputs, i.e., the EIT measurement and a binary mask image generated by the guidance image processing algorithm, whose input is an RGB microscopic image. The network then effectively fuses the information from the two different imaging modalities and generates the final conductivity image. We assess the performance of the proposed dual-modal framework by numerical simulation and MCF-7 cell imaging experiments. The results show that the proposed method could significantly improve image quality, indicating that impedance-optical joint imaging has the potential to reveal the structural and functional information of tissue-level targets simultaneously.