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Inverse Origami Design Model for Soft Robotic Development

Soft robotics 2024年第1期11卷 131-139页

作者： Hu, Qiqiang Li, Junyang Tao, Jian Dong, Erbao Sun, Dong Department of Biomedical Engineering City University of Hong Kong Hong Kong Department of Precision Machinery and Precision Instrumentation University of Science and Technology of China Hefei China Department of Electronic Engineering Ocean University of China Qingdao China CAS Key Laboratory of Mechanical Behavior and Design of Materials School of Engineering Science University of Science and Technology of China Hefei China Centre for Robotics and Automation Shenzhen Research Institute of City University of Hong Kong Shenzhen China

Origami provides an opportunity to construct a wide range of 3D functional structures by folding a flat sheet. It can be used to develop various soft functional robots by combining soft smart actuators. However, a simple and an effective model that can address the challenging problem of designing origami patterns to connect origami design with robotics is lacking, thereby greatly increasing the threshold of soft origami robots and hindering its development. This study proposes an easy-to-use inverse origami design model to generate the flat crease pattern from the desired folded shape automatically while simulating origami morphing by simply providing the shape parameters or 2D shape graphics. This method overcomes the difficulty of origami design and enables a close connection between origami and robotics. Through this method, various soft origami robots can be developed with low design complexity and time cost to achieve different functions, thereby promoting the development of soft origami robots. © 2024 Mary Ann Liebert Inc.. All rights reserved.

关键词： Robots

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Soft wearable flexible bioelectronics integrated with an anklefoot exoskeleton for estimation of metabolic costs and physical effort

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npj Flexible Electronics 2023年第1期7卷 535-544页

作者： Jihoon Kim Prakyath Kantharaju Hoon Yi Michael Jacobson Hyungkeun Jeong Hojoong Kim Jinwoo Lee Jared Matthews Nathan Zavanelli Hyeonseok Kim Heejin Jeong Myunghee Kim Woon-Hong Yeo IEN Center for Human-Centric Interfaces and Engineering and George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Department of Mechanical and Industrial Engineering the University of Illinois at ChicagoChicagoIL 60607USA Department of Mechanical Roboticsand Energy EngineeringDongguk UniversitySeoulRepublic of Korea Human Systems Engineering The Polytechnic SchoolIra A.Fulton Schools of EngineeringArizona State UniversityMesaAZ 85212USA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University School of MedicineAtlantaGA 30332USA Institute for Robotics and Intelligent Machines Parker H.Petit Institute for Bioengineering and BiosciencesInstitute for MaterialsNeural Engineering CenterGeorgia Institute of TechnologyAtlantaGA 30332USA

Activities and physical effort have been commonly estimated using a metabolic rate through indirect calorimetry to capture breath *** physical effort represents the work hardness used to optimize wearable robotic ***,personalization and rapid optimization of the effort are *** respirometry is the gold standard for estimating metabolic costs,this method requires a heavy,bulky,and rigid system,limiting the system’s field ***,this paper reports a soft,flexible bioelectronic system that integrates a wearable ankle-foot exoskeleton,used to estimate metabolic costs and physical effort,demonstrating the potential for real-time wearable robot adjustments based on *** from a set of activities,including walking,running,and squatting with the biopatch and exoskeleton,determines the relationship between metabolic costs and heart rate variability root mean square of successive differences(HRV-RMSSD)(R=−0.758).Collectively,the exoskeleton-integrated wearable system shows potential to develop a field-deployable exoskeleton platform that can measure wireless real-time physiological signals.

关键词： skeleton wear estimation

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LLM-RG4: Flexible and Factual Radiology Report Generation Across Diverse Input Contexts 39

LLM-RG4: Flexible and Factual Radiology Report Generation Ac...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Zhuhao Sun, Yihua Li, Zihan Yang, Xuan Chen, Fang Liao, Hongen School of Biomedical Engineering Tsinghua University Beijing China School of Biomedical Engineering Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)157735897X

Drafting radiology reports is a complex task requiring flexibility, where radiologists tail content to available information and particular clinical demands. However, most current radiology report generation (RRG) models are constrained to a fixed task paradigm, such as predicting the full “finding” section from a single image, inherently involving a mismatch between inputs and outputs. The trained models lack the flexibility for diverse inputs and could generate harmful, input-agnostic hallucinations. To bridge the gap between current RRG models and the clinical demands in practice, we first develop a data generation pipeline to create a new MIMIC-RG4 dataset, which considers four common radiology report drafting scenarios and has perfectly corresponded input and output. Secondly, we propose a novel large language model (LLM) based RRG framework, namely LLMRG4, which utilizes LLM's flexible instruction-following capabilities and extensive general knowledge. We further develop an adaptive token fusion module that offers flexibility to handle diverse scenarios with different input combinations, while minimizing the additional computational burden associated with increased input volumes. Besides, we propose a token-level loss weighting strategy to direct the model's attention towards positive and uncertain descriptions. Experimental results demonstrate that LLM-RG4 achieves state-of-the-art performance in both clinical efficiency and natural language generation on the MIMIC-RG4 and MIMIC-CXR datasets. We quantitatively demonstrate that our model has minimal input-agnostic hallucinations, whereas current open-source models commonly suffer from this problem. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Assistive robots for Beijing Winter Paralympic torch relay: Accessible technologies to restore human functionality

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The Innovation 2024年第1期5卷 19-20页

作者： Bi Zhang Mei Shuai Bicheng Han Shuo Li Lianqing Liu Xingang Zhao State Key Laboratory of Robotics Shenyang Institute of AutomationCASShenyang 110016China Institutes for Robotics and Intelligent Manufacturing CASShenyang 110169China Department of Biomedical Engineering Beihang UniversityBeijing 100191China Beijing AI-Robotics Technology Co. Ltd.Beijing 100176China Zhejiang BrainCo Technology Co. Ltd.Hangzhou 311121China

The success of the Beijing Winter Olympics and Paralympics epitomized the vision of a community with a shared future for *** robots and artificial intelligence technologies were used in various games and events,such as assisting in parades,logistics,and security *** integration of unmanned robotic systems into torch relays was one of the highlights of the Winter Olympics.

关键词： Winter Access artificial

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Real-time Stereoscopic Display of Atomic Force Microscopy Images for Nano Robotic Manipulation 18

Real-time Stereoscopic Display of Atomic Force Microscopy Im...

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18th International Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2023

作者： Liu, Xinyu So, Chun Ho Xue, Yuxuan Wang, Yichen Zhang, Jiawei Lai, King Wai Chiu Xi, Ning The University of Hong Kong Department of Industrial and Manufacturing Systems Engineering Hong Kong City University of Hong Kong Department of Biomedical Engineering Hong Kong Shenzhen Academy of Robotics Guangdong Shenzhen518000 China

ISBN: (纸本)9798350333480

We developed a nano-operating system that features a stereoscopic display of AFM images that is updated in real time. In order to accurately judge the positional relationship between the nanoparticles and carry out operations, the operator is able to use this system to not only observe the change process of the nano-environment in real time while the AFM is operating, but also obtain depth information with the help of a real-time stereoscopic display. At the same time, we give the operator with a corresponding perspective in real time by obtaining the changes in the operator's head posture. This helps to increase the sense of presence and improves operational efficiency. © 2023 IEEE.

关键词： Stereo image processing

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Leveraging Ultrasound Sensing for Virtual Object Manipulation in Immersive Environments

Leveraging Ultrasound Sensing for Virtual Object Manipulatio...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Keshav Bimbraw Jack Rothenberg Haichong Zhang Robotics Engineering Worcester Polytechnic Institute Worcester USA Robotics Engineering Biomedical Engineering Worcester Polytechnic Institute Worcester USA

Hand gesture recognition is a fundamental component of intuitive and immersive user interfaces in virtual reality (VR) applications. This paper presents a data-driven approach utilizing ultrasound data and deep learning techniques for hand gesture recognition in VR interfaces. The proposed methodology involves acquiring data from a subject, training a model using the acquired data, and evaluating the model’s performance on both the training data and during real-time inference. The evaluation metrics primarily focus on accuracy percentage: measuring the classifier’s performance in correctly classifying hand gestures. 4 hand gestures were primarily considered for the study and demonstration. For offline evaluation with a 20% test-train split, an accuracy percentage of 91% was observed. For online evaluation, an accuracy percentage of 92% was achieved. Results on the classification of 7 hand gestures were also analyzed for both online and online evaluation due to the promising results from the 4 gesture classification. The latency of the pipeline, from ultrasound data acquisition using screenshots to sending commands for VR object manipulation, was measured to be 59.48 milliseconds. The results demonstrate the effectiveness of the approach in accurately recognizing hand gestures, both during training and in real-time inference. We supplement our results with a video of the forearm ultrasound data being used to control a custom-designed VR game in a low-latency fashion. This research provides valuable insights into the performance and applicability of ultrasound-based hand gesture recognition techniques in VR interfaces. By employing deep learning and leveraging real-time data acquisition, this approach paves the way for intuitive and immersive interactions in various VR applications. The study contributes to the field of body sensor networks, highlighting the potential of forearm ultrasound based data-driven techniques for enhancing user interaction and immersion in VR

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Estimating Force Exerted by the Fingers Based on Forearm Ultrasound

Estimating Force Exerted by the Fingers Based on Forearm Ult...

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IEEE Symposium (IUS) Ultrasonics

作者： Keshav Bimbraw Haichong K. Zhang Robotics Engineering Worcester Polytechnic Insitute Worcester USA Robotics Engineering and Biomedical Engineering Worcester Polytechnic Insitute Worcester USA

Biosignal-based finger force estimation is an active area of research, with applications in teleoperation, human-machine interaction, and rehabilitation robotics. Traditionally, surface electromyography has been used to estimate hand grip and finger forces. In this paper, we show that forearm ultrasound can be used to estimate the force exerted by the fingers. A wireless ultrasound probe strapped to the forearm and a force sensor was used to estimate the ground truth. Accuracy percentages and root mean square error (RMSE) values were obtained for the shuffled and non-shuffled data subjected to a test-train split for all the fingers. It was found that the classification accuracy was 98.4 percent for the shuffled data, and 82 percent for the non-shuffled data averaged over all the fingers. For continuous estimation, the average RMSE was 0.02 N for the shuffled data and 0.2 N for the non-shuffled data. With a maximum force of 5 N, the average RMSE accounted for 4 percent of the maximum force for the non-shuffled data, and 0.4 percent for the shuffled data. These results show the potential of utilizing forearm ultrasound for estimating finger forces.

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State of Charge Algorithm Considering Open Circuit Voltage of LiFePO4 Battery Cells for Battery Energy Storage System

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Transactions of the Korean Institute of Electrical Engineers 2025年第5期74卷 969-976页

作者： Na, Kunwoo Choi, Keun Ha Kim, Wooyong Dept. of Biomedical & Robotics Engineering Incheon National University Korea Republic of Dept. of Mechanical Engineering Korea Advanced Institute of Science and Technology Korea Republic of

Lithium iron phosphate (LFP) batteries are increasingly adopted in electric vehicles (xEVs) and battery energy storage systems (BESS) due to their thermal stability, safety, and cost-effectiveness. Despite these advantages, the inherently low electrical conductivity and flat charge-discharge voltage profile of LFP batteries present significant challenges for accurate state estimation. To address these limitations, this study employs a Sigma-Point Kalman Filter (SPKF), which demonstrates superior performance compared to the conventional Extended Kalman Filter (EKF) in handling nonlinear system dynamics. Furthermore, to account for the hysteresis behavior characteristic of LFP cells, quasi-open circuit voltage (qOCV) measurements are utilized. A Dual Sigma-Point Kalman Filter (DSPKF) is proposed to enhance the estimation of both state of charge (SoC) and hysteresis states. The experimental results validate the effectiveness of the proposed method, offering a high-precision state estimation framework that improves the reliability and performance of LFP batteries in xEV and BESS applications. Copyright © The Korean Institute of Electrical Engineers.

关键词： State of charge

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Thermal Ablation Therapy Control with Tissue Necrosis-driven Temperature Feedback Enabled by Neural State Space Model with Extended Kalman Filter

Thermal Ablation Therapy Control with Tissue Necrosis-driven...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Ryo Murakami Satoshi Mori Haichong K. Zhang Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA The United States Department of Biomedical Engineering Robotics Engineering and Computer Science Engineering Worcester Polytechnic Institute Worcester MA The United States

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Thermal ablation therapy is a major minimally invasive treatment. One of the challenges is that the targeted region and therapeutic progression are often invisible to clinicians, requiring feedback provided in numerical information or imaging. Several emerging imaging modalities offer visualization of the ablation-induced necrosis formation; however, relying solely on necrosis monitoring can result in tissue overheating and endangering patients. Some of the necrosis monitoring modalities are known for their capabilities in temperature sensing, but the principles on which they are based have several limitations, such as sensitivity to the tissue motion and their environment. In this study, we propose a necrosis progression-based temperature estimation technique as an added safety feature for avoiding overheating. This model-based method does not require additional sensing hardware. It is designed to work as an independent estimator or a complimentary estimation component with other thermometers for improved robustness. For this objective, the Neural State Space model is used to approximate the ablation therapy, whose theoretical models involve nonlinear partial differential equations. Then, the Extended Kalman Filter is designed based on the model. The simulation study shows the estimation module robustly estimates the tissue temperature under several types of noise. The maximum estimation error observed before terminating ablation was around 1 °C, and the desired safety feature was successfully demonstrated. The estimator is expected to be used in a variety of necrosis monitoring modalities to guarantee more precise and safer treatment. More ambitiously, the architecture with the Neural State Space model and Extended Kalman Filter is generalizable to other medical/biological procedures involving nonlinear and patient/environment-specific physics and even to procedures having no reliable theoretical models.

关键词： Thermometers Estimation Medical treatment Mathematical models Robustness Sensors Safety Temperature control Kalman filters Monitoring

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Stress Detection using Wearable Physiological Sensors and Machine Learning Algorithm 6

Stress Detection using Wearable Physiological Sensors and Ma...

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6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Ashwin, V.H. Jegan, R. Rajalakshmy, P. Karunya Institute of Technology and Sciences Department of Biomedical Engineering Coimbatore India Karunya Institute of Technology and Sciences Department of Robotics Engineering Coimbatore India

ISBN: (纸本)9781665482714

The primary objective of this methodology is to develop and test the performance of a wearable psychological sensor using EEG, EDA and ECG to observe the stress level of humans and analyze if the observed variation in psychological signals is related to the objective biomarker of stress. An integrated wearable system with physiological sensors is designed, developed, tested and evaluated in this paper for monitoring stress using biological markers in the working environment. Stress detection is performed using the Muse S (Gen 2) EEG headset, Savvy ECG sensor, and Shimmer3 GSR sensors. For each subject under test, 32 features are extracted from the multi-modal signals, of which, five features are retained for EEG, four signals for ECG and 1 for EDA, summing up to 10 features. The window size for the collection of each feature is one minute. Hence, for the 20 participants and ten features, a data of 200 minutes is used as training data in a controlled environment. Of these, 138 minutes are labelled as stressful tasks and 62 minutes are labeled as no-stress. The proposed stress detection model effectively assesses stress under a controlled environment with an accuracy of 97% and in an everyday environment with an accuracy of 93%. This work aims to design and develop a remote control system that can be used in medical devices to prevent the aftereffects of stress. © 2022 IEEE.

关键词： Electrocardiograms

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