Abstract

Crosstalk, in a sensor matrix, is the unwanted signal obtained at a sensor pixel that is not directly related to the stimulus. This paper presents a novel approach towards quantifying the crosstalk characteristics of a sensor matrix. The method involves obtaining the normalized sum of all the neighbouring pixel readings, weighted by their distance from the stimulated pixel. We have used this methodology to characterise the crosstalk for a 5×5 velostat-based flexible pressure sensing matrix that uses a crossbar electrode architecture. The measure thus obtained is a dimensionless value between 0 to 1, 0 indicating no crosstalk and 1 indicating the maximum possible crosstalk. We characterized sensor matrices with three different pitch lengths: 3mm, 4mm, and 5mm. We observe the crosstalk values to lie between 0.032 to 0.17 across different weights and pitches, which indicates a low measure of crosstalk. The 5 mm pitch matrix displayed the least crosstalk, which was expected due to the larger spacing between the pixels. We also characterised the crosstalk for all 25 pixels of a 4 mm pitch matrix and found the mean to be 0.081±0.002 , within the range from 0.0071 to 0.1656.

Abstract

Polymer-based piezoresistive pressure sensors that possess flexibility and stretchability have received increased recognition in wearable sensing systems. In this paper, we present a flexible pressure sensor designed using polypyrrole-coated cotton (PCC) that was synthesized using in-situ chemical oxidative liquid polymerization. The sensor shows high sensitivity at low-pressure ranges and can measure pressures in the range 160 Pa to 16 kPa. The response time of the sensor was found to be 463 ms. It also exhibits excellent repeatability during continuous loading-unloading for over 1000 cycles. The performance of the sensor was evaluated in terms of resistance change as a function of tensile strain, by applying repeated compression and expansion strains. We report the gauge factor of the resistive sensor to be 0.32 for applied compressive strains from 0% to 99.6%. Such a large compressive strain range can be achieved with cotton because of its fibrous nature. It was observed that there is no change in conductivity, at a given strain, on repeated expansion and compression of the polymer for 20 cycles.

Abstract

Regular plantar pressure monitoring is required for diabetic, wheelchair-bound and bed-ridden individuals. It has been shown that peak plantar pressure for diabetics increases significantly and progressively, and requires regular monitoring for early detection and intervention. Sensor matrices of capacitative, resistive, and piezoelectric transducers are most commonly used for measuring areal pressure. Calibration for such medical devices is crucial for reliability and repeatable results. In this work, we elaborate on the calibration challenges of a 32×32 velostat-based sensor matrix, and demonstrate calibration strategies suitable for a manufactured product. We show that 2N parameters can be used to calibrate an N×N sensor matrix instead of 2N2 . We observe the accuracy of calibration of a 32×32 sensor matrix by comparing the total detected weight of 35 participants at a static load with their reported weight, with 4.2% mean error and 3.1% median error. For participants in the 50–85 kg weight range, the difference was always within ±5%. Further, upon drawing contour plots and using the pressure distribution information, we were able to distinguish participants with possible foot ulcers and flat feet.

Abstract

Bio-inspired structures provide several new possibilities in the area of robotics due to their superiority and diversity in execution. Researchers have been developing various robotic applications with the combination of soft materials such as silicone and various 3D printed materials. These kinds of soft robots can be useful for various human-robotic interactions such as search and rescue, medical surgery and so on. In this work, we present the fabrication and characterization of a small caterpillar-like soft robot (7 cm ×3.5 cm ×3 cm) powered using PneuNet actuation. Due to the limitations of traditional robotics, we believe that such soft material-based robots can be effective in various applications. The fabricated silicone-based PneuNet soft robot, which moves like a caterpillar, has also been embedded with an ESP camera module and LED light to enable applications such as surveillance and search-and-rescue. This soft robotic actuator can also perform tasks on various types of terrains, including ice and water surface. We find that the normalized distance travelled per actuation cycle on gravel to be 0.23 BL.

Abstract

Pressure sensors are subjected to continuous force and stress that may affect the operation of the sensor in the long run. Reliability is a crucial factor that must be considered when designing and fabricating any sensor. It is essential to test the material used in the sensor to assess the reliability of the complete product. In this work, we report the long-term reliability of a flexible pressure sensor mat using a carbon-impregnated polymer, velostat, which is a flexible, light, and thin polymer composite material with piezoresistive properties. We focus on the analysis of the performance of a flexible pressure sensor array under long-term and repeated loading. Tests were performed every fortnight for 210 days. We have observed that the material characteristics of the velostat material change on repeated application of pressure up to a certain time frame. For a given loading, once the material settles, the change in resistance of the material becomes consistent for a given application of pressure. We have also analyzed the changes in the parameters associated with the 2-parameter model, and have analyzed the effect of crosstalk on the sensor matrix for different pitch lengths to select the best pitch that will give us the minimum crosstalk. We have observed that the error rate of the sensor pixels decreased by 53 percentage points in 210 days. The results obtained from the experimental tests for reliability reveal a practical possibility of implementing velostat-based pressure sensors in wearable and healthcare devices and provide steps to take while calibrating an as-fabricated velostat-based sensor.

Abstract

Abstract—Building 3D maps of the environment is central to robot navigation, planning, and interaction with objects in a scene. Most existing approaches that integrate semantic concepts with 3D maps largely remain confined to the closed-set setting: they can only reason about a finite set of concepts, pre-defined at training time. Further, these maps can only be queried using class labels, or in more recent work, using text prompts. We address both these issues with ConceptFusion, a scene representation that is: (i) fundamentally open-set, enabling reasoning beyond a closed set of concepts (ii) inherently multimodal, enabling a diverse range of possible queries to the 3D map, from language, to images, to audio, to 3D geometry, all working in concert. ConceptFusion leverages the open-set capabilities of today’s foundation models that have been pretrained on internet-scale data to reason about concepts across modalities such as natural language, images, and audio. We demonstrate that pixel-aligned open-set features can be fused into 3D maps via traditional SLAM and multi-view fusion approaches. This enables effective zero-shot spatial reasoning, not needing any additional training or finetuning, and retains long-tailed concepts better than supervised approaches, outperforming them by more than 40% margin on 3D IoU. We extensively evaluate ConceptFusion on a number of real-world datasets, simulated home environments, a real-world tabletop manipulation task, and an autonomous driving platform. We showcase new avenues for blending foundation models with 3D open-set multimodal mapping. We encourage the reader to view the demos on our project page: https://concept-fusion.github.io

Abstract

Safe autonomous driving critically depends on how well the ego-vehicle can predict the trajectories of neighboring vehicles. To this end, several trajectory prediction algorithms have been presented in the existing literature. Many of these approaches output a multi-modal distribution of obstacle trajectories instead of a single deterministic prediction to account for the underlying uncertainty. However, existing planners cannot handle the multi-modality based on just sample-level information of the predictions. With this motivation, this paper proposes a trajectory optimizer that can leverage the distributional aspects of the prediction in a computationally tractable and sample-efficient manner. Our optimizer can work with arbitrarily complex distributions and thus can be used with output distribution represented as a deep neural network. The core of our approach is built on embedding distribution in Reproducing Kernel Hilbert Space (RKHS), which we leverage in two ways. First, we propose an RKHS embedding approach to select probable samples from the obstacle trajectory distribution. Second, we rephrase chance-constrained optimization as distribution matching in RKHS and propose a novel sampling-based optimizer for its solution. We validate our approach with hand-crafted and neural network-based predictors trained on real-world datasets and show improvement over the existing stochastic optimization approaches in safety metrics.

Abstract

Computer tomography (CT) have been routinely used for the diagnosis of lung diseases and recently, during the pandemic, for detecting the infectivity and severity of COVID-19 disease. One of the major concerns in using machine learning (ML) approaches for automatic processing of CT scan images in clinical setting is that these methods are trained on limited and biased subsets of publicly available COVID-19 data. This has raised concerns regarding the generalizability of these models on external datasets, not seen by the model during training. To address some of these issues, in this work CT scan images from confirmed COVID-19 data obtained from one of the largest public repositories, COVIDx CT 2A were used for training and internal validation of machine learning models. For the external validation we generated Indian-COVID-19 CT dataset, an open-source repository containing 3D CT volumes and 12096 chest CT images from 288 COVID-19 patients from India. Comparative performance evaluation of four state-of-the-art machine learning models, viz., a lightweight convolutional neural network (CNN), and three other CNN based deep learning (DL) models such as VGG-16, ResNet-50 and Inception-v3 in classifying CT images into three classes, viz., normal, non-covid pneumonia, and COVID-19 is carried out on these two datasets. Our analysis showed that the performance of all the models is comparable on the hold-out COVIDx CT 2A test set with 90%–99% accuracies (96% for CNN), while on the external Indian-COVID-19 CT dataset a drop in the performance is observed for all the models (8%–19%). The traditional machine learning model, CNN performed the best on the external dataset (accuracy 88%) in comparison to the deep learning models, indicating that a lightweight CNN is better generalizable on unseen data. The data and code are made available at https://github.com/aleesuss/c19.

Abstract

Deep learning assisted disease diagnosis using chest radiology images to assess severity of various respiratory conditions has garnered a lot of attention after the recent COVID-19 pandemic. Understanding characteristic features associated with the disease in radiology images, along with variations observed from patient-to-patient and with the progression of disease, is important while building such models. In this work, we carried out comparative analysis of various deep architectures with the proposed attention-based Convolutional Neural Network (CNN) model with customized bottleneck residual module (Attn-CNN) in classifying chest CT images into three categories, COVID-19, Normal, and Pneumonia. We show that the attention model with fewer parameters achieved better classification performance compared to state-of-the-art deep architectures such as EfficientNet-B7, Inceptionv3, ResNet-50 and VGG-16, and customized models proposed in similar studies such as COVIDNet-CT, CTnet-10, COVID-19Net, etc.

Abstract

Driving event recognition plays a crucial role in understanding and enhancing road safety. This research focuses on developing efficient time-series based models for Fall detection in two-wheelers. Traditional machine learning models proved inadequate in accurately classifying Fall scenarios due to their inability to capture temporal transitions in kinematic states. To address this limitation, time-series based Deep Learning (DL) models are proposed, utilizing Long Short-Term Memory (LSTM) networks. These networks enable direct learning from raw time series data, eliminating the need for manual feature engineering. Additionally, Bi-LSTMs were employed to capture contextual information from both past and future timesteps, further improving the model’s understanding of driving events. The architecture was enhanced with an attention mechanism to boost accuracy. Experimental results showcased that the proposed Bi-LSTM model achieved an overall accuracy of 97%, with a specific accuracy of approximately 92% in detecting Fall scenarios. This research contributes to the development of an accurate Time- series based system for Fall detection, facilitating improved road safety in the context of two-wheelers.