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1. Texture Discrimination using a Flexible Tactile Sensor Array on a Soft Biomimetic Finger

   https://doi.org/10.1109/SENSORS43011.2019.8956704  

   Sankar, Sriramana; Brown, Alisa; Balamurugan, Darshini; Nguyen, Harrison; Iskarous, Mark; Simcox, Talya; Kumar, Deepesh; Nakagawa, Andrei; Thakor, Nitish (October 2019, IEEE Sensors)

   null (Ed.)

   Soft robotic fingers provide enhanced flexibility and dexterity when interacting with the environment. The capability of soft fingers can be further improved by integrating them with tactile sensors to discriminate various textured surfaces. In this work, a flexible 3x3 fabric-based tactile sensor array was integrated with a soft, biomimetic finger for a texture discrimination task. The finger palpated seven different textured plates and the corresponding tactile response was converted into neuromorphic spiking patterns, mimicking the firing pattern of mechanoreceptors in the skin. Spike-based feature metrics were used to classify different textures using the support vector machine (SVM) classifier. The sensor was able to achieve an accuracy of 99.21% when two features, mean spike rate and average inter-spike interval, from each taxel were used as inputs into the classifier. The experiment showed that an inexpensive, soft, biomimetic finger combined with the flexible tactile sensor array can potentially help users perceive their environment better. 

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2. Passive Snapshot Coded Aperture Dual-Pixel RGB-D Imaging

   Ghanekar, Bhargav; Khan, Salman S; Sharma, Pranav; Singh, Shreyas; Boominathan, Vivek; Mitra, Kaushik; Veeraraghavan, Ashok (March 2024, ArXiv)

   Passive, compact, single-shot 3D sensing is useful in many application areas such as microscopy, medical imaging, surgical navigation, and autonomous driving where form factor, time, and power constraints can exist. Obtaining RGB-D scene information over a short imaging distance, in an ultra-compact form factor, and in a passive, snapshot manner is challenging. Dual-pixel (DP) sensors are a potential solution to achieve the same. DP sensors collect light rays from two different halves of the lens in two interleaved pixel arrays, thus capturing two slightly different views of the scene, like a stereo camera system. However, imaging with a DP sensor implies that the defocus blur size is directly proportional to the disparity seen between the views. This creates a trade-off between disparity estimation vs. deblurring accuracy. To improve this trade-off effect, we propose CADS (Coded Aperture Dual-Pixel Sensing), in which we use a coded aperture in the imaging lens along with a DP sensor. In our approach, we jointly learn an optimal coded pattern and the reconstruction algorithm in an end-to-end optimization setting. Our resulting CADS imaging system demonstrates improvement of >1.5dB PSNR in all-in-focus (AIF) estimates and 5-6% in depth estimation quality over naive DP sensing for a wide range of aperture settings. Furthermore, we build the proposed CADS prototypes for DSLR photography settings and in an endoscope and a dermoscope form factor. Our novel coded dual-pixel sensing approach demonstrates accurate RGB-D reconstruction results in simulations and real-world experiments in a passive, snapshot, and compact manner. 

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3. Energy-Efficient Missing Data Imputation in Wearable Health Applications: A Classifier-aware Statistical Approach

   https://doi.org/10.24963/ijcai.2024/807  

   Hussein, Dina; Belkhouja, Taha; Bhat, Ganapati; Doppa, Janardhan Rao (August 2024, International Joint Conferences on Artificial Intelligence Organization)

   Wearable devices are being increasingly used in high-impact health applications including vital sign monitoring, rehabilitation, and movement disorders. Wearable health monitoring can aid in the United Nations social development goal of healthy lives by enabling early warning, risk reduction, and management of health risks. Health tasks on wearable devices employ multiple sensors to collect relevant parameters of user’s health and make decisions using machine learning (ML) algorithms. The ML algorithms assume that data from all sensors are available for the health monitoring tasks. However, the applications may encounter missing or incomplete data due to user error, energy limitations, or sensor malfunction. Missing data results in significant loss of accuracy and quality of service. This paper presents a novel Classifier-Aware iMputation (CAM) approach to impute missing data such that classifier accuracy for health tasks is not affected. Specifically, CAM employs unsupervised clustering followed by a principled search algorithm to uncover imputation patterns that maintain high accuracy. Evaluations on seven diverse health tasks show that CAM achieves accuracy within 5% of the baseline with no missing data when one sensor is missing. CAM also achieves significantly higher accuracy compared to generative approaches with negligible energy overhead, making it suitable for wide range of wearable applications. 

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4. Surface Wrinkling for Flexible and Stretchable Sensors

   https://doi.org/10.1002/smll.202203491  

   Lee, Giwon; Zarei, Mohammad; Wei, Qingshan; Zhu, Yong; Lee, Seung Goo (September 2022, Small)

   Abstract Recent advances in nanolithography, miniaturization, and material science, along with developments in wearable electronics, are pushing the frontiers of sensor technology into the large‐scale fabrication of highly sensitive, flexible, stretchable, and multimodal detection systems. Various strategies, including surface engineering, have been developed to control the electrical and mechanical characteristics of sensors. In particular, surface wrinkling provides an effective alternative for improving both the sensing performance and mechanical deformability of flexible and stretchable sensors by releasing interfacial stress, preventing electrical failure, and enlarging surface areas. In this study, recent developments in the fabrication strategies of wrinkling structures for sensor applications are discussed. The fundamental mechanics, geometry control strategies, and various fabricating methods for wrinkling patterns are summarized. Furthermore, the current state of wrinkling approaches and their impacts on the development of various types of sensors, including strain, pressure, temperature, chemical, photodetectors, and multimodal sensors, are reviewed. Finally, existing wrinkling approaches, designs, and sensing strategies are extrapolated into future applications. 

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5. Sheet-based flexible technologies for mechanical sensing

   https://doi.org/10.1109/IFETC.2018.8583883  

   Kymissis, Ioannis; Yu, Caroline; Hsu, Yu-Jen; Piacenza, Pedro; Hannigan, Emily; Ciocarle, Matei; Allen, Peter (August 2018, 2018 International Flexible Electronics Technology Conference (IFETC))

   Flexible electronic technologies offer the potential for the co-integration of mechanical sensors that measure the state of the flexible surface under actuation or deformation. This format of sensor offers significant opportunities for the instrumentation of existing systems for a range of applications such as touch, measurement of acoustic field, and the detection of deformation modes of a system. Beyond the instrumentation of existing systems, flexible devices can themselves serve as actuators, allowing for sheet-based robotic devices, as well as the development of sensor formats for challenging applications. 

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