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IEEE International Conference on Rehabilitation Robotics (ICORR)

作者： Kim K. Peper Elisabeth R. Jensen Sami Haddadin Technical University of Munich Germany Department of Computer Engineering Chair of Robotics and Systems Intelligence TUM School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence (MIRMI)

The paper presents a solution to detect active ankle joint movement while a patient undergoes therapy with a robotic lower limb rehabilitation device that neither restricts nor actively supports ankle dorsi- or plantarflexion. The presented method requires the addition of only two accelerometer sensors to the system as well as a musculoskeletal model of the lower limb. Using forward kinematics and inverse dynamics, it enables knee and ankle joint kinematic tracking in the sagittal plane and muscle force estimation. This is an extension of a previous work in which only hip joint tracking was possible and, thus, muscle force estimation was limited. The correlation results of the current validation study with 12 healthy subjects show high correlation $(R=0.88\pm 0.09)$ between the kinematics estimated with the proposed method and those calculated from a gold standard motion capture setup for all three joints (hip, knee, and ankle). The correlation results of the estimated m. tibialis anterior muscle force against electromyography measurements $(R\ =\ 0.62\pm 0.27)$ are promising and a first application to a patient data set shows potential for future clinical application.

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Interweaving Artificial Intelligence and Bio-Signals in Mental Fatigue: Unveiling Dynamics and Future Pathways 20

Interweaving Artificial Intelligence and Bio-Signals in Ment...

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20th International computer Conference on Wavelet Active media technology and information Processing, ICCWAMTIP 2023

作者： Parveen, Saba Bin Heyat, Md Belal Akhtar, Faijan Parveen, Saima Asrafali, Barkathulla Singh, Bandana Ali, Kazim Huang, Lie Li, Xiao Peng Jabbar, Ayesha Asad, Maimoona Li, Jian Ping College of Electronics and Information Engineering Shenzhen University Shenzhen China CenBRAIN Neurotech Center of Excellence School of Engineering Westlake University Zhejiang Hangzhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shah Abdul Latif University Department of Mathematics Sindh Khairpur Pakistan College of Physics and Optoelectronics Engineering Shenzhen University Shenzhen China School of Media and Communication Shenzhen University Shenzhen China

ISBN: (纸本)9798350318982

This study conducts a thorough examination of machine learning models within Artificial Intelligence (AI)- integrated mental fatigue research, focusing on their identification and assessment. Investigating various biomedical signals linked to mental fatigue-such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR) this research sheds light on their roles in deciphering cognitive states and emotional responses. Through integration, these signals facilitate the creation of network visualizations, unveiling the intricate relationship between physiological markers and cognitive aspects during mental fatigue scenarios. Additionally, this study identifies gaps in current mental fatigue research with AI, emphasizing the need for an integrated approach across diverse domains and populations. It outlines future directions, suggesting avenues like multi-domain integration, personalized interventions, ethical considerations, longitudinal studies, and interdisciplinary collaborations to propel advancements in mental fatigue research, offering promising prospects for innovative AI-driven solutions in mental health and well-being. © 2023 IEEE.

关键词： Electrophysiology

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Enhancing Efficiency and Data Utility in Longitudinal Data Anonymization

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SSRN 2023年

作者： Amiri, Fatemeh Sánchez, David Domingo-Ferrer, Josep Universitat Rovira i Virgili Department of Computer Engineering and Mathematics CYBERCAT Center for Cybersecurity Research of Catalonia UNESCO Chair in Data Privacy Av. Paisos Catalans 26 Catalonia TarragonaE-43007 Spain Department of Computer Engineering and Information Technology Hamedan University of Technology Hamedan Iran

Longitudinal data are data collected over time on a set of individuals. The fact that information items are accumulated on each individual over time makes longitudinal data particularly privacy-sensitive. Existing anonymization methods are often ill-suited to ensure privacy-preserving publishing of this kind of data,and current privacy models assume unrealistic attacker knowledge. First, we propose the (k,\beta)^L-privacy model, which assumes the attacker's knowledge to be a subsequence of L quasi-identifiers, and thus provides a more realistic representation of the information actually available to attackers. This also contributes to enhancing the utility of the protected data. Second, we introduce FCLA, an anonymization algorithm to enforce our privacy model that prioritizes data utility while effectively mitigating identity and attribute disclosures, as well as skewness attacks in longitudinal data. FCLA partitions sequences into groups and anonymizes them independently, a task that is straightforward to parallelize. Reported experiments demonstrate that FCLA outperforms existing methods at preserving data utility while satisfying strict privacy constraints. The time complexity analysis and execution time of FCLA demonstrate that FLCA is more efficient, leading to better scalability. © 2023, The Authors. All rights reserved.

关键词： Privacy-preserving techniques

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Blockchain-Enhanced Energy-Efficient Architectures for Sustainable Internet of Things Ecosystems 1st

Blockchain-Enhanced Energy-Efficient Architectures for Susta...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Goyal, S.B. Rajawat, Anand Singh Verma, Chaman Illés, Zoltán Singh, Jaiteg Faculty of Information Technology City University Petaling Jaya46100 Malaysia School of Computer Sciences and Engineering Sandeep University Nashik India Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9783031860683

Novel technique are required to ensure the long-term viability and maximize the efficiency of energy consumption in the constantly changing Internet of Things (IoT) landscape. In order to accomplish these objectives, this study examines the potential use of blockchain technology into IoT architectural concepts. Our approach utilizes blockchain technology to effectively manage and enhance energy consumption in IoT devices and networks. This is achieved by capitalizing on the decentralized, secure, and transparent characteristics of blockchain. Our objective is to do this by developing a decentralized ledger system that documents all energy transactions and interactions inside an Internet of Things (IoT) environment. This strategy utilizes blockchain technology to provide accountability and real-time monitoring of energy consumption, hence facilitating improved energy allocation and consumption. We delve into the concept and execution of smart contracts that ensure transactions and automate energy-conservation procedures, thereby diminishing the necessity for centralized authority and alleviating energy wastage. In addition, we examine the possibility of blockchain technology to enable direct energy transfer between Internet of Things (IoT) devices. This feature facilitates the development of a self-sustaining Internet of Things ecosystem by enabling devices with surplus energy to vend it to those requiring it. We also tackle the difficulties associated with the integration of blockchain with IoT, such as the energy consumption resulting from blockchain activities and the ability to effectively handle large volumes of data. Furthermore, we offer resolutions to these problems. The proposed framework improves the energy efficiency of IoT ecosystems and promotes their sustainability by advocating for the use of renewable energy sources and decreasing carbon footprints. Through the utilization of simulations and real-world illustrations, we showcase the efficacy of our bl

关键词： Clean energy

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Improvement of voice quality using a digital filter to compensate for the spatial characteristics of MEMS microphone chambers

Improvement of voice quality using a digital filter to compe...

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作者： Kim, Hyun-Kab Kim, Gyu-Sik Lee, Jongseol Department of Electrical and Computer Engineering University of Seoul Seoul Korea Republic of Department of Electrical and Computer Engineering University of Seoul Seoul Korea Republic of Information and Media Research Center Korea Electronic Technology Institute Seoul Korea Republic of

Micro-electromechanical system (MEMS) microphones are sound sensors that are widely used in modern mobile and Internet-of-Things devices. Their market share is rapidly increasing due to their small size, excellent performance characteristics, and low power consumption. However, studies have focused on improving module performance without any spatial considerations. Most voice recognition devices have a small internal chamber in which a sensor is installed. A chamber with an open hole serves as a resonator, which alters the MEMS microphone's characteristics. The aim of this study is to improve voice quality by using a digital filter to compensate for the effect of the chamber. This will allow the identification of frequency-response compensation characteristics and the acquisition of base data that can be used for product design. © 2021 The Institute of Electronics and information Engineers

关键词： Frequency response

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Bridging Gaps in HCI: Advancing Education, Research, and Careers in Asia

Bridging Gaps in HCI: Advancing Education, Research, and Car...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Gamage, Dilrukshi Cheng, Shiwei Mudliar, Preeti Lu, Zhicong Zhao, Shengdong Ahmed, Nova Ma, Xiaojuan Lee, Uichin Wang, Ding Department of Computation and Intelligent Systems University of Colombo School of Computing Colombo Sri Lanka Zhejiang University of Technology Hangzhou China International Institute of Information Technology Bangalore Bangalore India Department of Computer Science City University of Hong Kong Hong Kong School of Creative Media City University of Hong Kong Hong Kong Design Inclusion and Access Lab. Department of Electrical and Computer Engineering North South University Dhaka Dhaka Bangladesh Hong Kong University of Science and Technology Hong Kong Hong Kong School of Computing KAIST Daejeon Korea Republic of Google AtlantaGA United States

ISBN: (纸本)9798400713958

Asia’s Human-computer Interaction (HCI) landscape is rapidly evolving, yet it faces distinct challenges in curriculum development, research establishment, and career navigation. This panel discussion, hosted by Asia SIGCHI Committee (ASC) at CHI2025, will bring together distinguished experts to address these challenges and explore strategies for fostering a robust and inclusive HCI community in Asia. Panelists will discuss the integration of culturally relevant content in HCI education, approaches to enhancing research visibility on international platforms, and initiatives to bridge gaps between academic training and industry expectations. Through an engaging discussion and audience interaction, the panel aims to identify actionable solutions, promote collaboration, and inspire future initiatives that strengthen HCI’s growth across the region. Participants will gain valuable insights into overcoming systemic barriers while building sustainable and impactful HCI programs tailored to Asia’s unique sociocultural dynamics. This panel seeks to advance the global relevance and contributions of HCI in Asia. © 2025 Copyright held by the owner/author(s).

关键词： Curricula

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To Sparsify or not to Sparsify: Simplifying Visual Feature Maps for Mobile Agents

To Sparsify or not to Sparsify: Simplifying Visual Feature M...

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IEEE International Symposium on Multimedia

作者： Sebastian Eger Rastin Pries Gábor Sörös Michael G. Adam Martin Piccolrovazzi Eckehard Steinbach Department of Computer Engineering Chair of Media Technology Munich Institute of Robotics and Machine Intelligence (MIRMI) School of Computation Information and Technology Technical University of Munich Nokia Bell Labs Munich Germany Nokia Bell Labs Budapest Hungary

ISBN: (纸本)9781665471732

Real-time pose estimation is crucial for autonomous agents for motion control and navigation, and it is essential for extended reality applications as well. As the current global positioning systems are not reliable indoors or are not precise enough, visual simultaneous localization and mapping is becoming prevalent in autonomous agents and mobile devices. The visual feature maps can be shared between agents and might be merged into a common map on a server and redistributed to all clients to enable re-localization and co-localization. However, merged maps can grow continuously in density and size that constrained agents are not able to handle *** this paper, we investigate how the map density effects localization and visual odometry performance on low-cost mobile clients. We show that a sparse representation of the original VFM is necessary for real-time visual odometry but at the cost of a degraded re-localization performance.

关键词： Visualization Simultaneous localization and mapping Mobile agents Pose estimation Real-time systems Mobile handsets Autonomous agents

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Distributed Combinatorial Optimization of Downlink User Assignment in mmWave Cell-free Massive MIMO Using Graph Neural Networks

Distributed Combinatorial Optimization of Downlink User Assi...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Bile Peng Bihan Guo Karl-Ludwig Besser Luca Kunz Ramprasad Raghunath Anke Schmeink Eduard A. Jorswieck Giuseppe Caire H. Vincent Poor Institute for Communications Technology Technische Universität Braunschweig Germany Department of Electrical and Computer Engineering Princeton University USA Chair of Information Theory and Data Analytics RWTH Aachen University Germany Communications and Information Theory Group Technische Universität Berlin Germany

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Millimeter wave (mmWave) cell-free massive MIMO (CF mMIMO) is a promising solution for future wireless communications. However, its optimization is non-trivial due to the challenging channel characteristics. We show that mmWave CF mMIMO optimization is largely an assignment problem between access points (APs) and users due to the high path loss of mmWave channels, the limited output power of the amplifier, and the almost orthogonal channels between users given a large number of AP antennas. The combinatorial nature of the assignment problem, the requirement for scalability, and the distributed implementation of CF mMIMO make this problem difficult. In this work, we propose an unsupervised machine learning (ML) enabled solution. In particular, a graph neural network (GNN) customized for scalability and distributed implementation is introduced. Moreover, the customized GNN architecture is hierarchically permutation-equivariant (HPE), i.e., if the APs or users of an AP are permuted, the output assignment is automatically permuted in the same way. To address this combinatorial problem, we relax it to a continuous problem, and introduce an information entropy-inspired penalty term. The training objective is then formulated using the augmented Lagrangian method (ALM). The test results show that the realized sum-rate outperforms that of the generalized serial dictatorship (GSD) algorithm and is very close to an upper bound in a small network scenario, while the upper bound is impossible to obtain in a large network scenario.

关键词： Wireless communication Training Upper bound Scalability Massive MIMO Machine learning Graph neural networks Millimeter wave communication Optimization Testing

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Aicaregaitrehabilitationbyfusion: Multi-Model and Multi-Modalities Sensor Data Fusion for Ai-Iot Enabled Realtime Electrical Stimulation Device for Pre-Fog and Post-Fog to Person with Parkinson's

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SSRN 2024年

作者： Ghayvat, Hemant Geddam, Rebakah Awais, Muhammad Tiwari, Prayag Milrad, Marcelo Kumar, Neeraj Computer Science and Media Technology Department Faculty of Technology Linnaeus University P G Vejdes väg 351 95 Växjö35234 Sweden Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahmedabad382481 India Department of Imaging Physics The University of Texas MD Anderson Cancer Center Houston77030 United States School of Information Technology Halmstad University Halmstad11042 Sweden Department of Computer Science and Engineering Thapar University Punjab Patiala India School of Computer Science University of Petroleum and Energy Studies Dehradun Uttarakhand Patiala147001 India

People with Parkinson's disease (PD) frequently experience the Freezing of Gait (FoG), a locomotive issue that reduces their quality of life and increases their risk of falling. These people are given timely bio-feedback cues to assist them in controlling their gait utilizing wearable FoG sensing technology. However, the currently used FoG detection methods are cumbersome and intrusive. As a result, the current study intends to incorporate the FoG detection method into a single sensor node using characteristics with low computational load and specialized hardware, which restricts the area and memory use. For the classification, a real-time learning neural network is used, allowing the method to adjust to the needs of a patient as needed. Machine learning techniques based on Support Vector Machines (SVM) and a tri-axial accelerometer are used in the research to design a new gadget for FoG detection that will be worn by the patient around their waist. The proposed device's categorization performance is highly competitive, comfortable to wear, and real-time adaptable. The findings indicate that an electrical stimulation-based cueing strategy has a beneficial, universal effect on gait and FoG in PD. Federated Learning (FL), a machine learning strategy, provides encouraging options for customization while protecting delicate health data. This study examines how federated learning can be used to wearable electrical stimulation devices for more effective, individualized Parkinson's treatment. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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ATRemix: An Auto-tune Remix Dataset for Singer Recognition 16th

ATRemix: An Auto-tune Remix Dataset for Singer Recognition

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16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Wang, Lifang Wang, Bingyuan Tan, Guanghao Zhang, Wei-Qiang Feng, Jun Zhu, Bing Wang, Shenjin Key Laboratory of Media Audio and Video Communication University of China Beijing100024 China School of Electronic Engineering and Computer Science Peking University Beijing100871 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China Department of Electronic Engineering Tsinghua University Beijing100084 China

ISBN: (纸本)9783031202322

In recent years, with the development of video websites such as YouTube, TikTok, and Bilibili, a great number of auto-tune remix audios are produced every day. Auto-tune remix audios are usually made from existing famous audios. The original clips can be tuned to various remixes through professional editing techniques. In the creation process, the characteristics of the singer are usually maintained, thus the original materials can be traced by singer recognition methods. This paper mainly focuses on the research of auto-tune remix singer recognition. As this topic has not been discussed before, we create a dataset of auto-tune remix audios and attempt to recognize the identity of the singer. Firstly, we use an x-vector model trained on the TIMIT dataset, and then evaluate it on the ATRemix dataset. Secondly, ATRemix dataset used to train different models, and SubATRemix dataset used as a test set, which shows good performance. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Statistical tests

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