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intelligent upper-limb exoskeleton integrated with soft bioelectronics and deep learning for intention-driven augmentation

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npj Flexible Electronics 2024年第1期8卷 856-868页

作者： Jinwoo Lee Kangkyu Kwon Ira Soltis Jared Matthews Yoon Jae Lee Hojoong Kim Lissette Romero Nathan Zavanelli Youngjin Kwon Shinjae Kwon Jimin Lee Yewon Na Sung Hoon Lee Ki Jun Yu Minoru Shinohara Frank L.Hammond Woon-Hong Yeo Department of Mechanical Roboticsand Energy EngineeringDongguk UniversitySeoul 04620Republic of Korea IEN Center for Wearable Intelligent Systems and Healthcare Institute for Electronics and NanotechnologyGeorgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGA 30332USA George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA School of Industrial Design Georgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Electronic Engineering Yonsei UniversitySeoul 03722Republic of Korea School of Biological Sciences Georgia Institute of TechnologyAtlantaGA 30332USA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University School of MedicineAtlantaGA 30332USA Institute for Materials Parker H.Petit Institute for Bioengineering and BiosciencesInstitute for Robotics and Intelligent MachinesGeorgia Institute of TechnologyAtlantaGA 30332USA

The age and stroke-associated decline in musculoskeletal strength degrades the ability to perform daily human tasks using the upper ***,we introduce an intelligent upper-limb exoskeleton system that utilizes deep learning to predict human intention for strength *** embedded soft wearable sensors provide sensory feedback by collecting real-time muscle activities,which are simultaneously computed to determine the user’s intended *** deep learning predicts four upper-limb joint motions with an average accuracy of 96.2%at a 500–550 ms response rate,suggesting that the exoskeleton operates just by human *** addition,an array of soft pneumatics assists the intended movements by providing 897 newtons of force while generating a displacement of 87mm at *** intent-driven exoskeleton can reduce human muscle activities by 3.7 times on average compared to the unassisted exoskeleton.

关键词： skeleton learning utilize

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Entropy Maximization in High Dimensional Multiagent State Spaces

Entropy Maximization in High Dimensional Multiagent State Sp...

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International Symposium on Multi-Robot and Multi-Agent systems (MRS)

作者： Ayhan Alp Aydeniz Enrico Marchesini Robert Loftin Kagan Tumer Collaborative Robotics and Intelligent Systems Institute Oregon State University Corvallis Oregon USA Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge Massachusetts USA Department of Computer Science University of Sheffield Sheffield United Kingdom

Underwater or planetary exploration are prime examples of missions that can benefit from autonomous agents working together. However, discovering effective team-level behaviors (i.e., coordinated joint actions) is challenging in these domains as agents typically receive a sparse reward (zero-or constant-for the majority of the interactions). To address this issue, intrinsic rewards encourage agents to explore diverse policies to visit the state space more effectively. Unfortunately, as the agents’ state space grows, intrinsic reward-based (i.e., curiosity) approaches become less effective as they cannot effectively distinguish a diverse set of states. In this direction, we introduce state entropy maximization for multiagent learning where agents explore using local (dense) rewards and learn to solve the coordination task by leveraging global (sparse) rewards. Because of the intrinsic ability to balance local and global rewards, our approach enables the state entropy function to remain effective in high dimensional state spaces. Experiments in tightly coupled tasks requiring complex joint actions, show that local entropy-based rewards enable agents to discover successful team behaviors in high dimensional spaces where previous hand-tuned count-based rewards fail.

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The Methodological Pitfall of Dataset-Driven Research on Deep Learning: An IoT Example

The Methodological Pitfall of Dataset-Driven Research on Dee...

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MILCOM, Military Communications Conference

作者： Tianshi Wang Denizhan Kara Jinyang Li Shengzhong Liu Tarek Abdelzaher Brian Jalaian Department of Computer Science University of Illinois at Urbana Champaign Urbana IL Department of Intelligent Systems and Robotics University of West Florida Pensacola FL 32514

In this paper, we highlight a dangerous pitfall in the state-of-the-art evaluation methodology of deep learning algorithms. It results in deceptively good evaluation outcomes on test datasets, whereas the underlying algorithms remain prone to catastrophic failure in practice. We illustrate the pitfall in the context of an Internet-of-Things (IoT) application example and show that it occurs despite the use of cross-validation that breaks down the data into separate training, validation, and testing sets. The pitfall is illustrated by designing two target detection and classification algorithms. One is based on a recently proposed neural network architecture for embedded AI, and the other is based on a traditional machine learning approach with domain-inspired feature engineering. The neural network approach outperforms the traditional one on test data. Yet, it fails in deployment. The mechanics behind the failure are explained and linked to the way the algorithms are trained. Suggestions are presented to avoid the pitfall. The paper is a “call to arms” to improve the evaluation methodology of machine learning algorithms for mission-critical systems.

关键词： Deep learning Training Military communication Feedback loop Machine learning algorithms Neural networks Mission critical systems

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LiDAR Simulation for Performance Evaluation of UAS Detect and Avoid

LiDAR Simulation for Performance Evaluation of UAS Detect an...

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2021 International Conference on Unmanned Aircraft systems, ICUAS 2021

作者： Riordan, James Manduhu, Manduhu Black, Julie Dow, Alexander Dooly, Gerard Matalonga, Santiago Almada Research Centre School of Computing Engineering and Physical Science University of the West of Scotland Lanarkshire United Kingdom Centre for Robotics and Intelligent Systems University of Limerick Department of Electronic and Computer Engineering

ISBN: (纸本)9780738131153

The solution to mitigating risks associated with beyond Visual Line of Sight (BVLOS) operations of Unmanned Aerial System (UAS) generally focuses on the use of advanced Unmanned Traffic Management (UTM) systems. However, this solution does not take into account other uncooperative objects in the airspace. A more robust approach is to have UTM integrations coupled with onboard machine vision which is tied to automated collision avoidance systems. Future BVLOS regulations in urban situations may require robust embedded software that is capable of detecting air collision hazards in realtime at near and far ranges as uncooperative small aircraft and other unpredictable small objects with fast-changing and unscheduled trajectories pose significant hazards to UAS. This work presents the concept and initial prototyping of a Digital Twin to evaluate the capability of UAS mounted LiDAR to detect small-object air collision risks. A Digital Twin of the Port of Hamburg is augmented with typical port and harbour aerial hazards such as birds, drones, helicopters, and low flying aircraft. The use case scenarios are created in Maya and Unity, with Optix ray tracing of typical LiDAR imaging configurations used to replicate the cause and effect relationship between different LiDAR specifications and their response to small flying objects. Our results demonstrate the inhomogeneous point clouds generated at different spatial-temporal parts of the LiDAR scanning cycle and field of view. These results confirm the challenges of detecting small uncooperative objects by LiDAR. © 2021 IEEE.

关键词： Hazards

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Deep Generative Models for Constructing Synthetic Populations in Public Health Applications

Deep Generative Models for Constructing Synthetic Population...

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Nathan Farmer Arash Mahyari Ashok Sirinivasan Department of Computer Science University of West Florida Pensacola FL USA Florida Institute For Human and Machine Cognition (IHMC) Pensacola FL USA Department of Intelligent Systems and Robotics University of West Florida Pensacola FL USA

There is a growing acknowledgment of the significance of social determinants of health in shaping fair public health policies. These determinants, reflected in emerging data streams like mobility and social media data, are increasingly integral to public health models. However, privacy concerns impede broad access to sensitive data, because even non-identifiable data are susceptible to deductive disclosure. To address this, synthetic populations trained on such data emerge as a privacy-conscious solution, offering the added benefit of exploring various "what-if" scenarios. This paper delves into the application of Generative Adversarial Networks (GANs) to craft synthetic populations that tackle privacy issues linked to social determinants of health data. Utilizing the 2019 PUMS dataset for Florida, the study trains the GAN to produce diverse and adaptable synthetic populations. An autoencoder manages the mix of continuous and categorical variables, ensuring the generated data aligns with categorical ranges. The results, assessed by comparing marginal distributions and employing tSNE, underscore the GAN's efficacy in balancing privacy and data utility. The significance of this work lies in identifying the promise of GANs in generating synthetic populations to advance health research.

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Embedding with Large Language Models for Classification of HIPAA Safeguard Compliance Rules

arXiv

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arXiv 2024年

作者： Rahman, Md Abdur Barek, Md Abdul Riad, A.B.M. Kamrul Islam Rahman, Md Mostafizur Rashid, Md Bajlur Ambedkar, Smita Miaa, Md Raihan Wu, Fan Cuzzocrea, Alfredo Ahamed, Sheikh Iqbal Dept. of Intelligent Systems and Robotics University of West Florida FL United States University of West Florida FL United States Department of Computer Science Tuskegee University TuskegeeAL United States IDEA Lab University of Calabria Rende Italy Department of Computer Science Marquette University WI United States

Although software developers of mHealth apps are responsible for protecting patient data and adhering to strict privacy and security requirements, many of them lack awareness of HIPAA regulations and struggle to distinguish between HIPAA rules categories. Therefore, providing guidance of HIPAA rules patterns classification is essential for developing secured applications for Google Play Store. In this work, we identified the limitations of traditional Word2Vec embeddings in processing code patterns. To address this, we adopt multilingual BERT (Bidirectional Encoder Representations from Transformers) which offers contextualized embeddings to the attributes of dataset to overcome the issues. Therefore, we applied this BERT to our dataset for embedding code patterns and then uses these embedded code to varioud machine learning approaches. Our results demonstrate that the models significantly enhances classification performance, with Logistic Regression achieving a remarkable accuracy of 99.95%. Additionally, we obtained high accuracy from Support Vector Machine (99.79%), Random Forest (99.73%), and Naive Bayes (95.93%), outperforming existing approaches. This work underscores the effectiveness and showcases its potential for secure application development. © 2024, CC BY.

关键词： Embeddings

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Airborne Sense and Detect of Drones using Deep Learning and LiDAR Point Clouds

arXiv

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arXiv 2023年

作者： Manduhu, Manduhu Dow, Alexander Trslic, Petar Dooly, Gerard Blanck, Benjamin Riordan, James Drone Systems Lab School of Computing Engineering and Physical Sciences University of the West of Scotland Glasgow United Kingdom Centre for Robotics & Intelligent Systems Department of Electronic & Computer Engineering University of Limerick Limerick Ireland Hamburg Port Authority Hamburg Germany

The safe operation of drone swarms beyond visual line of sight requires multiple safeguards to mitigate the risk of collision between drones flying in close-proximity scenarios. Cooperative navigation and flight coordination strategies that rely on pre-planned trajectories, constant network connectivity and reliable Global Navigation Satellite System (GNSS) positioning are brittle to failure. Drone embedded sense and detect offers a comprehensive mode of separation between drones for deconfliction and collision avoidance. This paper presents the first airborne LiDAR based solution for drone-swarm detection and localization using 3D deep learning model. It adapts an existing deep learning neural network to the air-to-air drone scenario by expanding the scan space vertically. A new sparse convolution is proposed and applied to accelerate the backbone layer, which is the most time-consuming part of the neural network. To collect training data of safety critical, close-proximity multi-drone operations, a scenario Digital Twin is used to augment real datasets with high fidelity synthetic data. The trained model achieves over 80% recall and 96% precision when tested on real-world datasets. By incorporating a tracking-by-detection algorithm the system can reliably monitor the separation distance of multiple drones in challenging environments. Copyright © 2023, The Authors. All rights reserved.

关键词： Drones

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Mitigating Insecure Outputs in Large Language Models(LLMs): A Practical Educational Module

Mitigating Insecure Outputs in Large Language Models(LLMs): ...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： MD Abdul Barek Md Mostafizur Rahman Shapna Akter A.B.M Kamrul Islam Riad Md Abdur Rahman Hossain Shahriar Akond Rahman Fan Wu Department of Intelligent Systems and Robotics University of West Florida USA Dept. of Cybersecurity and Information Technology University of West Florida USA Center for Cybersecurity University of West Florida USA Computer Science and Software Engineering Auburn University USA Dept. of Computer Science Tuskegee University USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Large Language Models (LLMs) have extensive ability to produce promising output. Nowadays, people are increasingly relying on them due to easy accessibility, rapid and outstanding outcomes. However, the use of these results without appropriate scrutiny poses serious security risks, particularly when they are integrated with other software, APIs, or plugins. This is because the LLM outputs are highly dependent on the prompts they receive. Therefore, it is essential to carefully clean these outputs before using them in additional software environments. This paper is designed to teach students about the potential dangers of contaminated LLM output within the context of web development through prelab, hands-on, and postlab experiences. Hands-on lab provides practical guidance on how to handle LLM vulnerabilities to make applications safe with some real-world examples in Python. This approach aims to provide students with a deeper understanding of the precautions necessary to ensure software against the vulnerabilities introduced by LLM output.

关键词： Training Navigation Large language models Computational modeling Software Security Software development management

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Enhance Planning with Physics-informed Safety Controller for End-to-end Autonomous Driving

Enhance Planning with Physics-informed Safety Controller for...

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IEEE International Conference on robotics and Biomimetics

作者： Hang Zhou Haichao Liu Hongliang Lu Jun Ma Yiding Ji Robotics and Autonomous Systems Thrust Systems Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Electronic and Computer Engineering School of Engineering The Hong Kong University of Science and Technology Hong Kong SAR China Intelligent Transportation Thrust Systems Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Recent years have seen a growing research interest in applications of Deep Neural Networks (DNN) on autonomous vehicle technology. The trend started with perception and prediction a few years ago and it is gradually being applied to motion planning tasks. Despite the performance of networks improve over time, DNN planners inherit the natural drawbacks of Deep Learning. Learning-based planners have limitations in achieving perfect accuracy on the training dataset and network performance can be affected by out-of-distribution problem. In this paper, we propose FusionAssurance, a novel trajectory-based end-to-end driving fusion framework which combines physics-informed control for safety assurance. By incorporating Potential Field into Model Predictive Control, FusionAssurance is capable of navigating through scenarios that are not included in the training dataset and scenarios where neural network fail to generalize. The effectiveness of the approach is demonstrated by extensive experiments under various scenarios on the CARLA benchmark.

关键词： Training Trajectory planning Artificial neural networks Benchmark testing System recovery Safety Planning Autonomous vehicles Robots Predictive control

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AuDimo: A Musical Companion Robot to Switching Audio Tracks by Recognizing the Users Engagement 22nd

AuDimo: A Musical Companion Robot to Switching Audio Tracks ...

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22nd International Conference on Human computer Interaction,HCII 2020

作者： Hansika, W.K.N. Nanayakkara, Lakindu Yasassri Gammanpila, Adhisha de Silva, Ravindra Robotics and Intelligent Systems Laboratory Department of Computer Science University of Sri Jayewardenepura Nugegoda Sri Lanka

ISBN: (纸本)9783030601164

Music has become an integral part that enriches human lives. It serves as a method of entertainment in social gatherings. Consequently Different playlists of music are played to energize, uplift, and comfort listeners in social gatherings. If most in that group of people don’t like the songs playing energy in between them can be lost. In this study, a musical companion robot was developed to interact with the audience through its rhythmic behaviors and through the song selections done by the robot based on the moods of the audience using Neural Networks and emotion recognition through facial recognition. The study demonstrated high levels of user acceptance based on the feedback received from a sample set of 15 volunteers within the age range of 21–25 years. Though The robot’s interaction through its rhythmic behaviors was a success, the song selection algorithm was below the level of acceptance at the startup of the robot. However, as the data demonstrates, the robot was able to accomplish the task of enhancing the audience through its appearance and behaviors. © 2020, Springer Nature Switzerland AG.

关键词： Music

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