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open datasets for Upper Limb Motion-A Systematic Review

IEEE ACCESS 2025年 13卷 74107-74127页

作者： Santanche, Ranieri Centrone, Antonia Mattei, Lorenza Di Puccio, Francesca Univ Pisa Dept Civil & Ind Engn I-56126 Pisa Italy Univ Pisa Ctr Univ Med Riabilitativa & Sport I-56126 Pisa Italy

open biomechanical datasets play a critical role in advancing scientific research, particularly with the growing use of machine learning, which requires a large amount of data for training and validation. However, the availability and characteristics of such datasets, particularly in the context of upper limb motion, are still underexplored. This systematic review aims to identify, assess, and categorize existing open datasets related to upper limb motion, focusing on the tasks analyzed, subject demographics, and data collection methods employed. We conducted a comprehensive search across multiple databases, including Scopus, Zenodo, PubMed, Xplore, and Google Dataset Search, selecting publicly accessible datasets that focus on upper limb motion based on predefined inclusion and exclusion criteria. Additional datasets were identified through manual searches. A total of 63 datasets met the criteria for further analysis, and their quality was assessed based on subject types, data collection methods, and sample sizes. The majority of the datasets centered on activities of daily living, with 69.8% of the studies involving healthy subjects. Marker-based motion capture was the most common data collection method, though the use of markerless systems (e.g., inertial sensors, video cameras) is on the rise. Key limitations included the absence of standardized movement protocols and small subject sample sizes across studies. In contrast to lower limb gait analysis, there is no single dominant task for studying upper limb motion. Standardizing movement protocols and incorporating a broader range of daily activities into datasets could enhance the development of assistive technologies and rehabilitation programs. Future research should focus on creating more standardized, diverse datasets to improve the accuracy and generalizability of biomechanical analyses.

关键词： Limbs Databases Sports Biomechanics Motion capture Sensors Protocols Sensor systems Biosensors Optical sensors Biomechanical databases open datasets human movements upper limb arm

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open datasets for AI-Enabled Radio Resource Control in Non-Terrestrial Networks 6

Open Datasets for AI-Enabled Radio Resource Control in Non-T...

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IEEE Future Networks World Forum (FNWF)

作者： Shahid, Husnain Vazquez, Miguel Angel Reynaud, Laurent Parzysz, Fanny Shaat, Musbah Ctr Tecnol Telecomunicac Catalunya CTTC CERCA Barcelona Spain Orange Labs Network Orange France

ISBN: (纸本)9798350324587

By effectively implementing the strategies for resource allocation, the capabilities, and reliability of nonterrestrial networks (NTN) can be enhanced. This leads to enhance spectrum utilization performance while minimizing the unmet system capacity, meeting quality of service (QoS) requirements and overall system optimization. In turn, a wide range of applications and services in various domains can be supported. However, allocating resources in a multi-constellation system with heterogeneous satellite links and highly dynamic user traffic demand pose challenges in ensuring sufficient and fair resource distribution. To mitigate these complexities and minimize the overhead, there is a growing shift towards utilizing artificial intelligence (AI) for its ability to handle such problems effectively. This calls for the development of an intelligent decision-making controller using AI to efficiently manage resources in this complex environment. In this context, real-world open datasets play a pivotal role in the development of AI models addressing radio control optimization problems. As a matter of fact, acquiring suitable datasets can be arduous. Therefore, this paper identifies pertinent real-world open datasets representing realistic traffic pattern, network performances and demand for fixed and dynamic user terminals, enabling a variety of uses cases. The aim of gathering and publishing the information of these datasets are to inspire and assist the research community in crafting the advance resource management solutions. In a nutshell, this paper establishes a solid foundation of commercially accessible data, with the potential to set benchmarks and accelerate the resolution of resource allocation optimization challenges.

关键词： open datasets Non-terrestrial Networks (NTN) Resource Management Artificial Intelligence

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DDoS in SDN: a review of open datasets, attack vectors and mitigation strategies

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DISCOVER APPLIED SCIENCES 2024年第9期6卷 472页

作者： Hill, Winston Acquaah, Yaa Takyiwaa Mason, Janelle Limbrick, Daniel Teixeira-Poit, Stephanie Coates, Carla Roy, Kaushik North Carolina Agr & Tech State Univ Dept Comp Sci Greensboro NC 27411 USA

Distributed denial of service (DDoS) attacks pose a significant threat to Software Defined Networking (SDN) and are frequently employed by malicious actors. SDN has emerged as a prominent networking paradigm, providing users with a decoupled control and data plane, which grants greater control and programmability over the network. In comparison to traditional networks, SDN offers dynamic, agile, cost-effective, and manageable solutions. However, a notable drawback of SDN is that the central controller becomes a vulnerable attack surface, rendering it susceptible to complete network takeover through DDoS attacks. The novelty of this paper is to gather resources that will be used to mitigate DDoS attacks in SDN environments. This paper focuses on the exploration of open datasets featuring DDoS attacks, as well as examining attack detection and mitigation techniques and frameworks. By analyzing various detection and mitigation strategies, network administrators and security professionals can make informed decisions to enhance the robustness and resilience of SDN environments in the face of evolving DDoS threats.

关键词： Software-defined networking Distributed denial of service attacks open datasets

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Dreem open datasets: Multi-Scored Sleep datasets to Compare Human and Automated Sleep Staging

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2020年第9期28卷 1955-1965页

作者： Guillot, Antoine Sauvet, Fabien During, Emmanuel H. Thorey, Valentin Algorithm Team Dreem F-75002 Paris France French Armed Forces Biomed Res Inst IRBA Fatigue & Vigilance Unit F-91220 Bretigny Sur Orge France Paris Descartes Univ VIFASOM EA 7330 F-75006 Paris France Stanford Univ Ctr Sleep Sci & Med Stanford CA 94304 USA

Sleep stage classification constitutes an important element of sleep disorder diagnosis. It relies on the visual inspection of polysomnography records by trained sleep technologists. Automated approaches have been designed to alleviate this resource-intensive task. However, such approaches are usually compared to a single human scorer annotation despite an inter-rater agreement of about 85% only. The present study introduces two publicly-available datasets, DOD-H including 25 healthy volunteers and DOD-O including 55 patients suffering from obstructive sleep apnea (OSA). Both datasets have been scored by 5 sleep technologists from different sleep centers. We developed a framework to compare automated approaches to a consensus of multiple human scorers. Using this framework, we benchmarked and compared the main literature approaches to a new deep learning method, SimpleSleepNet, which reach state-of-the-art performances while being more lightweight. We demonstrated that many methods can reach human-level performance on both datasets. SimpleSleepNet achieved an F1 of 89.9% vs 86.8% on average for human scorers on DOD-H, and an F1 of 88.3% vs 84.8% on DOD-O. Our study highlights that state-of-the-art automated sleep staging outperforms human scorers performance for healthy volunteers and patients suffering from OSA. Considerations could be made to use automated approaches in the clinical setting.

关键词： Sleep apnea Feature extraction Brain modeling Machine learning Electroencephalography Manuals Automated sleep stage classification deep learning PSG EEG open datasets inter-rater agreement

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FUSION NETWORK AND open ACCESS DATASET FOR LANDSLIDE DETECTION: A COMPARATIVE ANALYSIS ON BIJIE AND HOKKAIDO datasets

FUSION NETWORK AND OPEN ACCESS DATASET FOR LANDSLIDE DETECTI...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Lissak, Candide Corpetti, Thomas Univ Rennes Res Inst Environm & Occupat Hlth IRSET INSERMEHESPUMR S 1085 F-35000 Rennes France Univ Rennes Univ Brest Nantes Univ CNRSUMR LETG 6554 F-35000 Rennes France

ISBN: (纸本)9798350360332;9798350360325

Remote sensing techniques are increasingly employed for early detection of ground deformation and landslide warning systems. The combination of vast remote sensing data and advancements in machine learning algorithms has led to significant progress in landslide detection and mapping. This study proposes an innovative neural network architecture for landslide detection. The network utilizes a fusion of optical images (RGB) and Digital Elevation Models (DEMs) to enhance accuracy. Additionally, attention layers and Mixup techniques are incorporated to further improve the model's performance. Given the limited availability of training data, the proposed network was trained on a publicly accessible dataset, specifically the established Bijie landslide dataset located in China. We demonstrate that using an efficient training strategy on this dataset allows us to pretrain a network that can be easily fine-tuned for a different site exhibiting landslides at various scales. In practice, we introduce a newly developed dataset covering the southwestern part of Hokkaido, Japan, which experienced a landslide event in 2018. This dataset is freely available on the internet. The results demonstrate the effectiveness of the proposed architecture compared to existing methods, highlighting the benefits of our pretrained model.

关键词： Landslide detection fusion network attention open datasets

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open and free datasets for multimedia retrieval

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INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL 2016年第3期5卷 135-136页

作者： Bakker, Erwin M. Leiden Univ LIACS Media Lab Leiden Netherlands

One of the pillars of the scientific community is open and free datasets. Not only do they allow benchmarking, evaluation and also reproducibility but they also provide an important contribution themselves by allowing researchers to gain deeper insight into the strengths and weaknesses of their algorithms and paradigms. Furthermore, in the cases of the larger datasets, they facilitate major advances (e.g., concept learning using big data). Here, we present some of the recent free and open datasets for the scientific community.

关键词： open datasets Multimedia information retrieval Freely redistributable Evaluation Performance

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EEG datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

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SN Computer Science 2023年第5期4卷 437页

作者： Handa, Palak Mathur, Monika Goel, Nidhi Department of ECE DTU Delhi India Department of ECE IGDTUW Delhi India

Epilepsy is a common non-communicable, group of neurological disorders affecting more than 50 million individuals worldwide. Researchers are working to automatically detect epileptic activities through Electroencephalography (EEG) signal analysis, and artificial intelligence techniques. Good quality, open-source, and free EEG data acts as a catalyst in the on-going research to early diagnose this disease. The present review focuses on reporting EEG datasets for automatic epilepsy diagnosis and seizure detection for the past three decades. Upon a thorough search analysis, 28 publicly available, and private EEG datasets have been compiled and compared for adult and paediatric human populations suffering from epilepsy. The discussion of the advantages and limitations of the present datasets has been done based on several parameters. Their associated, recent experimental pipelines have also been discussed. CHB-MIT and Bonn remain the benchmark datasets in this field. EEG meta-data has been released to tackle large EEG datasets like CHB-MIT and Siena Scalp. In conclusion, an increasing trend in the release of open-source EEG datasets has been observed with varied clinical setups and demographics. A standardized data development protocol is needed to develop generalized artificial intelligence techniques for the early detection of epileptic events. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Automatic epilepsy diagnosis Biomedical multimedia EEG signals open datasets Performance Seizure detection

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Systematic Evaluation of Personalized Deep Learning Models for Affect Recognition

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PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT 2024年第4期8卷 1-35页

作者： Han, Yunjo Zhang, Panyu Park, Minseo Lee, Uichin Korea Adv Inst Sci & Technol Daejeon South Korea Sungkyunkwan Univ Suwon South Korea

Understanding human affective states such as emotion and stress is crucial for both practical applications and theoretical research, driving advancements in the field of affective computing. While traditional approaches often rely on generalized models trained on aggregated data, recent studies highlight the importance of personalized models that account for individual differences in affective responses. However, there remains a significant gap in research regarding the comparative evaluation of various personalization techniques across multiple datasets. In this study, we address this gap by systematically evaluating widely-used deep learning-based personalization techniques for affect recognition across five open datasets (i.e., AMIGOS, ASCERTAIN, WESAD, CASE, and K-EmoCon). Our analysis focuses on realistic scenarios where models must adapt to new, unseen users with limited available data, reflecting real-world conditions. We emphasize the principles of reproducibility by utilizing open datasets and making our evaluation models and codebase publicly available. Our findings provide critical insights into the generalizability of personalization techniques, the data requirements for effective personalization, and the relative performance of different approaches. This work offers valuable contributions to the development of personalized affect recognition systems, fostering advancements in both methodology and practical application.

关键词： Affective Computing Deep Learning Personalization open datasets Reproducibility

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Data Driven Forecasting Models for Urban Air Pollution: MoreAir Case Study

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IEEE ACCESS 2023年 11卷 133131-133142页

作者： Berkani, Safaa Gryech, Ihsane Ghogho, Mounir Guermah, Bassma Kobbane, Abdellatif Int Univ Rabat TICLab Rabat 11103 Morocco Katholieke Univ Leuven Dept Elect Engn B-3000 Leuven Belgium Mohammed V Univ Rabat ENSIAS Rabat 10102 Morocco Univ Leeds Fac Engn Leeds LS2 9JT England

Artificial Intelligence has the potential to contribute to sustainable cities, life on land, and climate action. Specifically, data-driven AI models can analyze large, interconnected databases to develop joint environmental actions. Air quality plays a pivotal role in both climate action and the development of sustainable cities, but developing countries face challenges due to insufficient monitoring stations and limited access to air quality data sets. This study builds upon the MoreAir project, which established a low-cost air pollution monitoring system and provided the first air quality data set from Morocco. We first exploit and delve into the details of the obtained dataset. Subsequently, we conduct a multi-level comparison of data-driven forecasting models, specifically focusing on short-term forecasting of Particulate Matter concentrations. Four forecasting frameworks are explored, using different combinations of exogenous data and spatio-temporal information. Our findings highlight that Machine Learning models, particularly LightGBM and CatBoost, outperform other models. Overall, our study demonstrates that the inclusion of the spatial dimension along with the diverse exogenous features enhances the models' predictive performance, and provides valuable insights.

关键词： Atmospheric modeling Forecasting Monitoring Predictive models Biological system modeling Air pollution Data models Pollution measurement Environmental monitoring Deep learning urban air pollution forecasting open datasets statistical models machine learning deep learning

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VALUE: Visual Analytics driven Linked data Utility Evaluation

VALUE: Visual Analytics driven Linked data Utility Evaluatio...

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Workshop on Human-In-the-Loop Data Analytics (HILDA)

作者： Bhattacharjee, Kaustav Dasgupta, Aritra New Jersey Inst Technol Newark NJ 07102 USA

ISBN: (纸本)9798400702167

The widespread adoption of open datasets across various domains has emphasized the significance of joining and computing their utility. However, the interplay between computation and human interaction is vital for informed decision-making. To address this issue, we first propose a utility metric to calibrate the usefulness of open datasets when joined with other such datasets. Further, we distill this utility metric through a visual analytic framework called VALUE, which empowers the researchers to identify joinable datasets, prioritize them based on their utility, and inspect the joined dataset. This transparent evaluation of the utility of the joined datasets is implemented through a human-in-the-loop approach where the researchers can adapt and refine the selection criteria according to their mental model of utility. Finally, we demonstrate the effectiveness of our approach through a usage scenario using real-world open datasets.

关键词： open datasets linking utility visual analytics

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