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21st IEEE International Carpathian Control Conference, ICCC 2020

作者： Sikora, Wojciech Grzybek, Dariusz Kata, Dariusz Sapinski, Bogdan Agh University of Science and Technology Faculty of Mechanical Engineering and Robotics Cracow Poland Agh University of Science and Technology Faculty of Materials Science and Ceramics Cracow Poland

ISBN: (数字)9781728119519

ISBN: (纸本)9781728119519

One of the factors limiting performance of PZT material in energy harvesting applications is its tensile strength, which limits its maximum allowable strain. In typical cantilever beam harvester the piezoelectric patch placed on the beam is periodically stretched and compressed during vibrations. Exceeding specific value of tensile stress leads to damage of piezoelectric material and impairment of its energy conversion capabilities. One of the distinct properties of all ceramics, also PZT, is exhibiting low strength in tension but, simultaneously, very high compressive strength. Aim of this work was to introduce initial compressive stress into piezoelectric layer, therefore increasing allowable beam deflection during vibration. To determine effect of piezoelectric patch prestressing on harvester performance a series of numerical simulations were done. © 2020 IEEE.

关键词： Cantilever beams

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Lower Limb Trajectory Prediction for Exoskeleton with AutoEncoder-Deep Gaussian Process

Lower Limb Trajectory Prediction for Exoskeleton with AutoEn...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Pingping Lv Chi Zhang Feng Yi Ting Yuan Shupei Li Meitong Zhang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Chinese Academy of Sciences Ningbo China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Ningbo China

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

Gait trajectory prediction is a crucial component in compensating control system delays, reducing interactive resistance, and achieving real-time assistance in exoskeleton robotics. In this paper, an adaptive gait prediction model based on the autoencoder deep Gaussian process (AE-DGP) is proposed for joint angle estimation using measurement data from inertial measurement units (IMUs). The DGP process involves stochastic gradient Hamiltonian Monte Carlo (SGHMC) calculations and Monte Carlo Expectation-maximization (MCEM) optimization at a lower computational cost. The kernel of the model is the multi-feature fusion input based on autoencoder and the construction of the DGP model with an adaptive sliding window. To suppress spikes in predicted deviations, a weighted averaging strategy is employed to estimate future stable gait trajectories. Benefiting from the design, the proposed model can enhance the accuracy and adaptability of gait prediction at different speeds. Experiments involving four participants at four speeds indicate that the proposed method achieves hip and knee joint prediction with less than 0.16° and 0.35° root mean squared errors respectively. Compared to the Gaussian process (GP) and long short-term memory (LSTM) models, the proposed method exhibits superior performance in adaptive gait trajectory prediction at different speeds.

关键词： Adaptation models Monte Carlo methods Computational modeling Exoskeletons Gaussian processes Predictive models Real-time systems

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Preliminary Validation of an Immersive Virtual Reality Framework for Prosthetic Embodiment Quantification

Preliminary Validation of an Immersive Virtual Reality Frame...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Johanna Happold Diogo Morais Patricia Capsi-Morales Cristina Piazza School of Computation Information and Technology Technical Univer-sity of Munich (TUM) Munich Germany Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich (TUM) Munich Germany Faculty of Science University of Lisbon Lisbon Portugal

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

This work introduces an immersive virtual reality framework designed for the assessment of both functionality and sense of embodiment of myoelectric upper limb prostheses. The framework features separate training and testing environments in which participants are asked to grasp and release objects with different dimensions controlling a virtual hand with eight sEMG sensors. In the testing session, we introduced a new metric for objective quantification of embodiment based on the shift in users' attention. We evaluated the proposed methodology with ten able-bodied participants and one par-ticipant with limb loss, collecting pre- and post-training data within multiple sessions. Results suggest a similar trend of the proposed virtual reality assessment method and standard questionnaire-based measures of embodiment for able-bodied participants. Moreover, we found a positive correlation between functionality and subjective embodiment highlighting the need for appropriate functional training. Future investigations will include longitudinal studies exploring the integration of our virtual reality framework in real-world rehabilitation settings.

关键词： Training Correlation Virtual reality Sensor phenomena and characterization Particle measurements Market research Standards Robots Testing Prosthetics

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Defining, measuring, and modeling passenger's in-vehicle experience and acceptance of automated vehicles

arXiv

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arXiv 2023年

作者： Bhide, Neeraja Hashimoto, Nanami Dokurno, Kazimierz Van der Hoorn, Chris Hoogendoorn-Lanser, Sascha Nordhoff, Sina Delft Center for Systems and Control Delft University of Technology Netherlands Department of Cognitive Robotics Delft University of Technology Netherlands Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Department Transport & Planning Delft University of Technology Netherlands

Automated vehicle acceptance (AVA) has been measured mostly subjectively by questionnaires and interviews, with a main focus on drivers inside automated vehicles (AVs). To ensure that AVs are widely accepted by the public, ensuring the acceptance by both drivers and passengers is key. The in-vehicle experience of passengers will determine the extent to which AVs will be accepted by passengers. A comprehensive understanding of potential assessment methods to measure the passenger experience in AVs is needed to improve the in-vehicle experience of passengers and thereby the acceptance. The present work provides an overview of assessment methods that were used to measure a driver's behavior, and cognitive and emotional states during (automated) driving. The results of the review have shown that these assessment methods can be classified by type of data-collection method (e.g., questionnaires, interviews, direct input devices, sensors), object of their measurement (i.e., perception, behavior, state), time of measurement, and degree of objectivity of the data collected. A conceptual model synthesizes the results of the literature review, formulating relationships between the factors constituting the in-vehicle experience and AVA acceptance. It is theorized that the in-vehicle experience influences the intention to use, with intention to use serving as predictor of actual use. The model also formulates relationships between actual use and well-being. A combined approach of using both subjective and objective assessment methods is needed to provide more accurate estimates for AVA, and advance the uptake and use of AVs. © 2023, CC BY.

关键词： Vehicles

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A Deep Reinforcement Learning-based Routing Algorithm for Unknown Erroneous Cells in DMFBs

A Deep Reinforcement Learning-based Routing Algorithm for Un...

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Annual IEEE Northeast Workshop on Circuits and Systems (NEWCAS)

作者： Tomohisa Kawakami Chiharu Shiro Hiroki Nishikawa Xiangbo Kong Hiroyuki Tomiyama Shigeru Yamashita Graduate School of Science and Engineering Ritsumeikan University Japan Graduate School of Information Science and Technology Osaka University Japan Department of Intelligent Robotics Faculty of Engineering Toyama Prefectural University Japan College of Information Science and Engineering Ritsumeikan University Japan

A Digital Microfluidic Biochip (DMFB) is a kind of biochips, which is used in various kinds of fields, e.g., DNA analysis, clinical diagnosis, PCR testing and so on. Biochips can automatically carry out biochemical experiments, which makes the biochemical experiments smaller, faster and easier to use rather than previous ones. However, biochips have the problem that they cannot adapt sudden errors, which makes the reliability of the experiments in biochips lower. Such errors can be categorized into two kinds; One is call known errors and the other is called unknown errors. The known errors are the ones that noticed before the routing process starts by sensors or cameras. On the other hand, the unknown errors indicate that they can be noticed only during the routing process, and these kinds of unknown errors cannot be noticed by sensors or cameras. Therefore, there is the challenge to deal especially with the unknown errors. In this paper, we propose a deep reinforcement learning-based routing algorithm for the two kinds of errors. In the experiments, we demonstrate the effectiveness of ours in all the cases, including known errors and unknown errors. In addition, the algorithm contributes to detecting unknown errors during the routing process, and shows the shortest routing path with high probability.

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Portable Baby Incubator Monitoring using Smartphone

Portable Baby Incubator Monitoring using Smartphone

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International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM)

作者： Nur Sultan Salahuddin Sri Poernomo Sari Nora Trisna Tumewa Aqilla Rahman Musyaffa Arif Wijaya Ema Herawati Satrio Dwi Yudhanto Ambariani Pujiati Faculty of Computer Science and Information Technology Gunadarma University Jakarta Indonesia Faculty of Industrial Technology Gunadarma University Jakarta Indonesia Multimedia And Robotics Laboratory Gunadarma University Depok Indonesia Industrial Automation Laboratory Gunadarma University Depok Indonesia Faculty of Health Sciences and Pharmacy Gunadarma University Depok Indonesia

Most neonatal deaths occur at the age of 0-6 days (78.5%) and prematurity is one of the main causes of infant death in Indonesia, ranking fifth in the number of premature births. A baby incubator is very necessary to help premature babies adapt to their new environment. An incubator is a health care tool for critical/premature neonates that provides warmth and humidity according to the needs of newborns under controlled conditions. A portable baby incubator monitoring system using wifi as a communication medium to monitor babies, equipped with a DS18B20 sensor to measure the baby’s body temperature, an AD8232 sensor to measure the baby’s heartbeat and an ESP32 Cam to capture the baby’s face has been created. The test results showed that this incubator was able to detect the baby’s body temperature, the baby’s heartbeat and take the baby’s face remotely using an Android-based smartphone, weighs around 5.8 kg, has a simple design, can be carried around making it easier for medical personnel to carry out supervision premature baby.

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Effects of “Robotic Rocking Pillow” on Human Short Sleep at Noon

Effects of “Robotic Rocking Pillow” on Human Short Sleep a...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Kazutaka Yamamoto Yuuichi Kawamura Shima Okada Yusuke Sakaue Masanobu Manno Masaaki Makikawa Graduate School of Science and Engineering Ritsumeikan University Shiga Japan AxRobotix Corporation Tokyo Japan Department of Robotics Faculty of Science and Engineering Ritsumeikan University Shiga Japan Ritsumeikan Global Innovation Research Organization Ritsumeikan University Shiga Japan Research Organization of Science and Technology Ritsumeikan University Shiga Japan

Sleep is an essential element for achieving well-being. The Rocking Phenomenon has been shown to improve sleep quality and sleep onset, and the vestibular system has been shown to mediate this effect. Rocking is often performed on a rocking bed, which is large in size. The lying body needs to be rocked up and down or right to left side in order for the rocking phenomenon to occur. A large device is needed to rock the body of an adult. Therefore, existing studies have used a method of rocking the bed from right to left side. However, the rocking phenomenon only requires the stimulation of the vestibular organs, which are the sensory organs of the human body. Therefore, it may be possible to produce the rocking effect by rocking the pillow. Therefore, we conduct a one-hour napping experiment to confirm whether rocking of only the head can induce the rocking effect. The results show that delta wave tends to increase and RMSSD becomes smaller as the respiratory frequency is prolonged. We will conduct an all-night experiment to verify the structure of sleep changes even during long periods of sleep.

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Image and Command Transmission Over the 5G Network for Teleoperation of Mobile Robots

Image and Command Transmission Over the 5G Network for Teleo...

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Iberian robotics Conference (ROBOT)

作者： Thiago B. Levin João Miguel Oliveira Ricardo B. Sousa Manuel F. Silva Bruno S. Parreira Héber Miguel Sobreira Hélio Sousa Mendonça CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Portugal Faculty of Engineering University of Porto (FEUP) Portugal ISEP Polytechnic of Porto. Portugal NOS SGPS Portugal

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

Human oversight can benefit scenarios with complex tasks, such as pallet docking and loading and unloading containers, beyond the current capabilities of autonomous systems without any failures. Furthermore, teleoperation systems allow remote control of mobile ground robots, especially with the surge of 5G technology that promises reliable and low latency communication. Current works research on exploring the latest features from the 5G standard, including ultra-Reliable Low-Latency Communication (uRLLC) and network slicing. However, these features may not be available depending on the Internet Service Provider (ISP) and communication devices. Thus, this work proposes a network architecture for the teleoperation of ground mobile robots in industrial environments using commercially available devices over the 5G Non-Standalone (NSA) standard. Experimental results include an evaluation of the network and End-to-End (E2E) latency of the proposed system. The results show that the proposed architecture enables teleoperation, achieving an average E2E latency of 347.19 ms.

关键词： 5G mobile communication Network slicing Web and internet services Ultra reliable low latency communication Mobile robots Reliability Surges Standards Remote control Pallets

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arXiv

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arXiv 2025年

作者： Fidêncio, Aline Xavier Grün, Felix Klaes, Christian Iossifidis, Ioannis Robotics and BCI Laboratory Institute of Computer Science Ruhr West University of Applied Sciences Mülheim an der Ruhr Germany Faculty of Electrical Engineering and Information Technology Ruhr University Bochum Bochum Germany KlaesLab Department of Neurosurgery University Hospital Knappschaftskrankenhaus Ruhr University Bochum Bochum Germany Faculty of Computer Science Ruhr University Bochum Bochum Germany

Brain-computer interfaces (BCIs) are developed for individuals with motor disabilities to offer alternative methods of communication, such as controlling and interacting with external devices. These systems are designed to improve quality of life, particularly for those with limited mobility. BCIs that rely on non-invasive recordings, such as electroencephalography (EEG), have been developed for a wide range of scenarios due to their practicality and safety. However, BCI performance is often limited by non-stationarities in the EEG data, which can arise from changes in the subject’s mental state or device characteristics, such as electrode impedance. These challenges motivate ongoing research into the development of adaptive BCIs capable of handling such variations. Over the past years, the interest in using the so-called error-related potentials (ErrPs) to improve BCI performance has increased. This is because ErrPs represent the neural response to both self-made and external errors and can be measured using non-invasive techniques. These signals have been combined with different BCI paradigms and used in different works to improve BCI performance via error correction or adaptation. In this work we introduce and explore a new approach for creating an adaptive ErrP-based BCI by evaluating the use of reinforcement learning (RL). This study demonstrates the feasibility of a RL-driven adaptive brain-computer interface (BCI) framework that integrates error-related potentials (ErrPs) and motor imagery. By employing two RL agents to dynamically adapt to EEG non-stationarities, we validate the framework on both a publicly available motor imagery dataset and using a novel experimental protocol involving a fast-paced game designed to enhance user engagement. Results highlight the framework’s potential: RL agents successfully learned control policies from user interactions, achieving robust performance across datasets. However, a critical finding emerged from the game-based p

关键词： Reinforcement learning

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Classification of Gait Phases by Using SVM and ANN Based on EMG Signals

Classification of Gait Phases by Using SVM and ANN Based on ...

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IEEE Symposium on Industrial Electronics & Applications (ISIEA)

作者： Nurhazimah Nazmi Shin-Ichiroh Yamamoto Muhammad Amirul Sunni Rohim Ezreen Farina Shair Center for Artificial Intelligence and Robotics (CAIRO) Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Bio-Science and Engineering Shibaura Institute of Technology Saitama Japan Faculty of Electrical Engineering Universiti Teknikal Malaysia Melaka Melaka Malaysia

ISBN: (数字)9798350386868

ISBN: (纸本)9798350386875

Advanced technology in rehabilitation aims to improve gait patterns through innovative mechanisms and powerful motors. Interestingly, a good performance of the control system of those devices can be achieved when it is paired with a functional gait phase detection algorithm using wearable sensors such as electromyography (EMG) signals. Since emerging machine learning in EMG signals has a significant impact on the development of exoskeletons, machine learning such as artificial neural networks (ANN) has been widely utilized, especially for gait patterns and gait phases. Although support vector machines (SVM) are seen as having great potential for interpreting EMG signals, few studies have been observed in gait phases, especially stance and swing. Therefore, this study proposes a classification gait phase by using SVM and compares the performance with ANN. Combinations of two and more of the five time domain (TD) features were extracted from EMG signals and fed into the SVM and ANN models. Then, the SVM and ANN models with different kernel functions and training algorithms were compared, respectively. As a result, combinations of all five TD features enhanced the classification accuracy more than two or fewer combinations of TD features. Besides, SVM with a radial basis function (RBF) achieved better performance than a linear function with 98% accuracy. This model also performed better than ANN, which only gained up to 95.8% of classification accuracy. Thus, this study demonstrates that SVM is not only able to discriminate between stance and swing phases but also improves the accuracy of gait phases. Therefore, SVM with an RBF kernel function should be considered for analyzing EMG signals in near future.

关键词： Support vector machines Training Accuracy Biological system modeling Artificial neural networks Machine learning Feature extraction

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