As healthcare shifts toward prevention and wearable monitoring scales to millions of devices, the cumulative environmental footprint of disposable electrode materials and inefficient data processing becomes a critical design concern. We address this through two complementary approaches: sustainable bioimpedance electrode materials and resource-efficient on-device signal processing. Our evaluation of 20 screen-printed electrode configurations on PLA, recycled PET, and cellulose substrates shows that copper paste electrodes match or exceed conventional $\text{Ag} / \text{AgCl}$ in bioimpedance signal quality (regression 0.97 vs 0.93). An on-device signal processing block extracts heart rate and respiratory rate with Polar H10 reference-device disagreement of 2.66 BPM and 0.805 BrPM respectively, using only 3.5 KB Flash and 8.9 KB DRAM per extraction block on an ESP32-C3-Mini. These results demonstrate that practical vital sign extraction and sustainable electrode materials can together enable population-scale preventive monitoring with substantially reduced environmental impact.

The desire to increase the energy of the pulses by which the impact excitation of ultrawideband antennas is carried out prompts some authors of equipment (radars) to change the shape of these pulses. In particular, pulses are made bipolar. This article explores the issue of optimizing the shift of the second component of the excitation pulse. Such a shift allows obtaining the maximum level of the spectrum at the frequency of interest, determined by the transmitter-receiver path.

This paper accompanies the initial public release of the EDI multi-modal SLAM dataset, a collection of long tracks recorded with a portable sensor package. These include two global shutter RGB camera feeds, LiDAR scans, as well as inertial and GNSS data from an RTK-enabled IMU-GNSS positioning module—both as satellite fixes and internally fused interpolated pose estimates. The tracks are formatted as ROS1 and ROS2 bags, with separately available calibration and ground truth data. In addition to the filtered positioning module outputs, a second form of sparse ground truth pose annotation is provided using independently surveyed visual fiducial markers as a reference. This enables the meaningful evaluation of systems that directly utilize data from the positioning module into their localization estimates, and serves as an alternative when the GNSS reference is disrupted by intermittent signals or multipath scattering. In this paper, we describe the methods used to collect the dataset, its contents, and its intended use.

We introduce a novel wired communication approach for interactive wearable systems, employing a single signal wire and innovative group addressing protocol to reduce overhead. While wireless solutions dominate body sensor networks, wired approaches offer advantages for interactive applications that require low latency, high reliability, and communication with high-density nodes; yet they have been less explored in the context of wearable systems. Many commercial products use wired connections without disclosing technical details, limiting broader adoption. To address this gap, we present and test a new group addressing protocol implemented using Universal Asynchronous Receiver–Transmitter (UART) hardware, disclosing frame diagrams and node architectures. We developed a prototype interactive jacket with nine sensor/actuator nodes connected via three wires for power supply and data transmission to a wireless gateway. Mathematical analysis showed an overhead reduction of approximately 50% compared to traditional individual addressing. Our solution is the most wire-efficient among wired interactive wearable systems reviewed in the literature, using only one signal wire; other methods require at least two wires and often have overlapping topologies. Performance experimental evaluation revealed a total feedback delay of 2.27 ms and a maximum data frame rate of 435.4 Hz, comparable to the best-performing products and leaving room for twice the performance calculated theoretically. These results indicate that the proposed approach is suitable for interactive wearable systems, both for real-time applications and high-resolution data acquisition.

As natural language processing advances in the field of robotics, enabling seamless human-robot interaction, it becomes imperative to identify the most effective approach for conditioning complex robotics tasks using natural language commands.This article reviews various state-of-the-art methods for natural language-conditioned planning, with a particular focus on mobile manipulation.The authors explore and review different architectures and techniques to comprehend, interpret, and execute natural language com mands.Challenges are identified along the way, and conceptual architecture is proposed to tackle them in an efficient manner.

This paper outlines a conceptual design for a multi-level natural language-based planning system and describes a demonstrator. The main goal of the demonstrator is to serve as a proof-of-concept by accomplishing end-to-end execution in a real-world environment, and showing a novel way of interfacing an LLM-based planner with open-set semantic maps. The target use-case is executing sequences of tabletop pick-and-place operations using an industrial robot arm and RGB-D camera. The demonstrator processes unstructured user prompts, produces high-level action plans, queries a map for object positions and grasp poses using open-set semantics, then uses the resulting outputs to parametrize and execute a sequence of action primitives. In this paper, the overall system structure, high-level planning using language models, low-level planning through action and motion primitives, as well as the implementation of two different environment modeling schemes—2.5 or fully 3-dimensional—are described in detail. The impacts of quantizing image embeddings on object recall are assessed and high-level planner performance is evaluated using a small reference scene data set. We observe that, for the simple constrained test command data set, the high-level planner is able to achieve a total success rate of 96.40%, while the semantic maps exhibit maximum recall rates of 94.69% and 92.29% for the 2.5d and 3d versions, respectively.

This paper motivates and describes a methodology for annotating SLAM benchmark data sets where GPS or motion capture equipment is not viable. Ground truth camera pose measurements are obtained at the camera frame rate using surveyed Apriltag2 visual markers. The design of the marker plates, procedures for placing and positioning them, as well as the procedures for sensor track collection, are described. An estimate of the measurement accuracy is obtained by comparison with the Optitrack motion capture system, and the advantage over RTK-corrected GPS as a ground truth pose measurement source in certain environmental conditions is demonstrated experimentally.

In the pursuit of enhancing healthcare through biomedical engineering and wearable technology, this research conducts a thorough investigation into the efficacy of different electrodes for Bioelectrical Impedance (BioZ) and Body-Coupled Communication (BCC) applications. We meticulously analyze Ag/AgCl, Electrical Muscle Stimulation (EMS), and gold-plated electrodes across a variety of metrics such as Signalto-Noise Ratio (SNR), Settling Time, and Chip Error Rate (CER). Through a series of experiments tailored to assess their performance in monitoring heart rate, breathing, and facilitating data transmission through the human body, we uncover the specific contexts in which each electrode type excels. This study highlights the critical role of electrode choice in advancing non-invasive diagnostic methods through wearable technology. It provides a comprehensive comparison of various electrode types, highlighting their respective benefits for specific health monitoring applications. This research aims to guide the future development of wearable devices, aiming for an optimal balance between sustainability, accuracy, and user convenience in electrode selection for BioZ measurement and BCC applications.

This document describes the results of the study contributing to the methods and tools applicable in plastic waste sorting systems that exploit the multistatic ultra-wideband impulse radar enforced with a deep learning signal processing back-end. The novelty of the research is the use of synthetic data for the development of a trained neural network before real data are available, and the use of a multistatic radar for the improvement of the training data set. The study results are described in multiple publications; the current paper shows the applicability of the described approach. The main results are as follows: a monostatic impulse radar can be used for the determination of material properties, such as thickness, dielectric permittivity, and losses, with limited accuracy; multistatic radar configuration increases the accuracy of the material property estimation; an open source finite difference time domain simulator can be used to simulate electromagnetic wave propagation in dielectric structures in order to generate synthetic data for development of optimized artificial neuron network structures used for the estimation of dielectric material properties, and the developed network can successfully be used for multistatic radar data processing.

This paper examines the possibility of using low-cost commercial off-the-shelf audio recording equipment in combination with machine learning techniques to discover the presence of hostile UAVs. A convolutional neural network (CNN) was trained to detect and localize two types of quadrotor drones using ground truth position data collected with motion capture equipment. System performance was evaluated on pre-recorded validation data sets and in realtime operation. In both cases, drones can be successfully detected and localized within the constrained working volumes studied, achieving angular accuracies in the 8–13° range. However, further work remains to be done before system feasibility in outdoor conditions can be established.

Artificial neural networks are becoming more popular with the development of artificial intelligence. These networks require large amounts of data to function effectively, especially in the field of computer vision. The quality of an object detector is primarily determined by its architecture, but the quality of the data it uses is also important. In this study, we explore the use of novel data set enhancement technique to improve the performance of the YOLOv5 object detector. Overall, we investigate three methods: first, a novel approach using synthetic object replicas to augment the existing real data set without changing the size of the data set; second - rotation augmentation data set propagating technique and their symbiosis, third, only one required class is supplemented. The solution proposed in this article improves the data set with a help of supplementation and augmentation. Lower the influence of the imbalanced data sets by data supplementation with synthetic yeast cell replicas. We also determine the average supplementation values for the data set to determine how many percent of the data set is most effective for the supplementation.

In contemporary biomedical research, the accurate automatic detection of cells within intricate microscopic imagery stands as a cornerstone for scientific advancement. Leveraging state-of-the-art deep learning techniques, this study introduces a novel amalgamation of Fuzzy Automatic Contrast Enhancement (FACE) and the You Only Look Once (YOLO) framework to address this critical challenge of automatic cell detection. Yeast cells, representing a vital component of the fungi family, hold profound significance in elucidating the intricacies of eukaryotic cells and human biology. The proposed methodology introduces a paradigm shift in cell detection by optimizing image contrast through optimal fuzzy clustering within the FACE approach. This advancement mitigates the shortcomings of conventional contrast enhancement techniques, minimizing artifacts and suboptimal outcomes. Further enhancing contrast, a universal contrast enhancement variable is ingeniously introduced, enriching image clarity with automatic precision. Experimental validation encompasses a diverse range of yeast cell images subjected to rigorous quantitative assessment via Root-Mean-Square Contrast and Root-Mean-Square Deviation (RMSD). Comparative analyses against conventional enhancement methods showcase the superior performance of the FACE-enhanced images. Notably, the integration of the innovative You Only Look Once (YOLOv5) facilitates automatic cell detection within a finely partitioned grid system. This leads to the development of two models-one operating on pristine raw images, the other harnessing the enriched landscape of FACE-enhanced imagery. Strikingly, the FACE enhancement achieves exceptional accuracy in automatic yeast cell detection by YOLOv5 across both raw and enhanced images. Comprehensive performance evaluations encompassing tenfold accuracy assessments and confidence scoring substantiate the robustness of the FACE-YOLO model. Notably, the integration of FACE-enhanced images serves as a catalyst, significantly elevating the performance of YOLOv5 detection. Complementing these efforts, OpenCV lends computational acumen to delineate precise yeast cell contours and coordinates, augmenting the precision of cell detection.

Muscle fatigue is a common symptom that many people experience and is associated with difficulties in voluntary movement, which can lead to injuries. Currently, surface electromyography (sEMG) is considered the gold standard for muscle fatigue estimation, but its accuracy can be impacted by various factors. Therefore, new methods, such as the use of inertial sensors (IMU), are being introduced. This study aimed to explore the relationship between muscle fatigue and biomechanical parameters using inertial sensors and sEMG as a validation tool. Four participants performed an elbow flexion exercise, and the data from IMU sensor nodes and sEMG were collected. The results showed that there were correlations between the electrical activity of m. biceps brachii and rotation angles of the forearm and upper arm. Additionally, an increase in motion amplitude deviation was found to be a potential indicator of muscle fatigue. These findings suggest that inertial sensors can be used as an alternative to sEMG for detecting muscle fatigue, which has potential implications for injury prevention and rehabilitation. However, further research with a larger sample size is needed to validate these findings.

Mapping the environment is a powerful technique for enabling autonomy through localization and planning in robotics. This article seeks to provide a global overview of actionable map construction in robotics, outlining the basic problems, introducing techniques for overcoming them, and directing the reader toward established research covering these problem and solution domains in more detail. Multiple levels of abstraction are covered in a non-exhaustive vertical slice, starting with the fundamental problem of constructing metric occupancy grids with Simultaneous Mapping and Localization techniques. On top of these, topological meshes and semantic maps are reviewed, and a comparison is drawn between multiple representation formats. Furthermore, the datasets and metrics used in performance benchmarks are discussed, as are the challenges faced in some domains that deviate from typical laboratory conditions. Finally, recent advances in robot control without explicit map construction are touched upon.

Motion control platforms have various applications in the manufacturing and automation industries. Different literature provides multiple issues related to the kinematics and dynamics of self-guided robots for transportation regarding platform balancing. Self-balancing platforms are utilized in many deliveries, stabilization, and transportation systems, and they are especially well suited for outdoor activities when the ground surface is not flat or structured. This paper describes developing a control technique for a self-balancing platform using the 3-RCC spherical parallel manipulator. This mechanism was designed to support an AGV (Automated Guided Vehicle) for transporting and lifting heavy weights for industrial applications. The AGV carries a robotic arm on top for different tasks. When the AGV encounters a steep slope or a rough surface, the AGV tilts, and the robotic arm’s performance is significantly affected. So, this study gives a solution to avoid these circumstances with a novel approach for the platform’s self-balancing mechanism consisting of a 3-RCC spherical parallel manipulator. Real-time stabilization and kinematics analysis methods are used to achieve the self-balancing system of the platform. When both methods are observed through different tilting angles for automation stability, Kinematic analysis performs more efficiently with less time duration when compared with the real-time stabilization method.