Flexible Data Research Articles

Exploring the optimal sequences for fusing land surface temperature via spatiotemporal fusion: ‘retrieval-then-fusion’ or ‘fusion-then-retrieval’?

ABSTRACT Due to the limitations inherent in satellite sensor capabilities, it remains a challenge to balance the temporal and spatial resolutions of current remote sensing observations. Spatiotemporal fusion has emerged as a viable approach to address this issue. Many spatiotemporal fusion models have been proposed to fuse surface reflectance initially; however, their adaption for land surface temperature (LST), particularly with the ‘fusion-then-retrieval (FTR)’ or ‘retrieval-then-fusion (RTF)’ strategy, has not yet been comprehensively explored. This paper describes a comparative study of the two strategies within the context of LST fusion using three classic fusion methods including the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion model (FSDAF). The findings illuminate nuanced applicability across different models: (1) the FTR strategy is more suitable for STARFM; (2) the strategic applicability for ESTARFM and FSDAF is strongly related to the reference images; (3) the STARFM and FSDAF models are easily affected by the time interval between the predicted date and the reference date. This work is expected to provide practical guidance for synthesizing high spatiotemporal LST data with fusion models.

Improving Unplugged Computational Thinking Skills Through Integrated Problem-Based and Differentiated Learning in Indonesia

Industry 4.0 era is characterized by the development of technology and the existence of flexible and effective data exchange. This has an impact on the emergence of new jobs that require the ability to solve problems effectively and efficiently, especially computational thinking. This Classroom Action Research aims to improve students’ computational thinking (abstraction, data collection, data analysis and algorithms) in solving problems about probability through problem-based learning integrated with differentiation learning. This research was conducted in four stages: planning, implementation, observation, and reflection. Thirty-four students of class X-E7 SMA Negeri 5 Surakarta in Indonesia were the subjects of this research. It was concluded that the application of problem-based learning model integrated with differentiated learning (content and process) significantly improves students’ computational thinking skills and promotes cognitive development through problem-solving activities. The improvement of computational thinking ability is caused by: data collection activities in the form of dice/coin simulations, abstraction activities of sample points and important information in the problem, activities to formulate solution steps (algorithms) and data analysis activities in the form of interpretation of results and calculations. Differentiated learning (content and process) plays a role in improving students’ computational thinking through ability-based scaffolding and content based on their interests and learning modalities.

Legal Liaison

The Legal Liaison system is an innovative web-based application designed to transform the landscape of legal practice and management. By incorporating essential functionalities for various stakeholders including clients, advocates, law students, junior advocates, and administrative staff, this system significantly enhances the efficiency and effectiveness of legal services. This application facilitates easy interaction and management for users by providing a centralized platform for all legal activities. It supports client engagement, advocate-client communication, case management, and administrative oversight, ensuring a streamlined workflow for legal professionals. Additionally, it promotes the professional development of emerging legal talents through structured mentorship and resource sharing. By leveraging modern technology, the Legal Liaison system aims to simplify complex legal processes, foster better collaboration, and improve resource utilization, ultimately providing a more accessible and efficient legal service environment. This holistic approach ensures that all participants in the legal ecosystem can benefit from enhanced coordination and optimized legal practices The front end of the Legal Liaison system is built using React.js, ensuring a responsive and interactive user experience. The backend employs Express.js and Node.js to create a robust and scalable server-side environment. MongoDB acts as the database, offering flexible and scalable data storage. Combined, these technologies form a seamless and efficient platform for contemporary legal practice

DiSSCover the Potential of FAIR Digital Object Annotations and How You Can Use Them!

The infrastructure for the Distributed System of Scientific Collections (DiSSCo) is in full development. Work within the DiSSCo Transition Project has been focused on building infrastructure, creating data models, and setting up Application Programming Interfaces (APIs) (Koureas et al. 2024). In the past years, DiSSCo has presented this work at different Biodiversity Information Standards (TDWG) conferences (Leeflang and Addink 2023, Leeflang et al. 2022, Addink et al. 2021). In this year’s session, we would like to focus on the human-facing application: DiSSCover. DiSSCover is the graphical user interface through which users can interact with Findable, Accessible, Interoperable and Reusable (FAIR) Digital Objects (FDOs), facilitating the curation and enhancement of specimen data (Islam 2024). Development started in 2022 and is ongoing. The interface acts as a gateway into the DiSSCo infrastructure, providing access to digital specimens and media. Extracted from the core DiSSCo API, the data is converted into an easily readable format and made discoverable through a diverse set of filters. DiSSCover’s main focus is to allow users to make annotations upon the data. Through the concept of annotations, we connect expert and machine-generated information, to create extended digital specimens (Hardisty et al. 2022), e.g, by creating linkages to other infrastructures, correcting or adding new information, or by triggering machine annotation services. Machine annotation services are automated scalable tools that run in the background and automatically curate and extend the specimen (Addink et al. 2023). Human users will remain important, as all annotations made by machine annotation services can be reviewed by a trusted person. Annotations are Fair Digital Objects and target a specific part of a specimen, be it a data fragment or an associated media file. At the heart of DiSSCover lies the Open Digital Specimen data specification (Leeflang and Addink 2023). It tries to harmonise multiple data standards into one generic specification based on the new Global Biodiversity Information Facility (GBIF) Unified Model (Robertson et al. 2022). The data is stored as JavaScript Object Notation (JSON) based on JSON Schemas (Anonymous 2024). Annotations are linked to specific data attributes using a JSON-path as the identifier. Data attributes can be individual terms, collections of terms called classes, or the whole object. This creates a flexible but complex data structure, the basis for which we used the World Wide Web Consortium (W3C) web annotation data model (Sanderson et al. 2017). The W3C Web annotation data model contains two main components: the target and body. The target specifies which data attribute the annotation is made on, for example, the term: ‘ods:specimenName’. This is a local term within the open Digital Specimen namespace (ods), which holds the accepted name of the digital specimen. The annotation body holds the value(s) that are appended to the digital specimen, and differ based upon the annotation motivation. DiSSCo recognises five different annotation motivations: addition, modification, comment, assessment and deletion, each of which has its own unique function. This creates a flexible structure that should be able to handle any information the user wants to add to the object. The challenge of DiSSCover is to preserve the complex structure of annotations, whilst making it convenient for users to work with. The session will provide a look at the different kinds of annotations and their use from a practical perspective. A demonstration of DiSSCover will show how users can create annotations, providing knowledge about the process that will give shape to DiSSCo’s main goal of enriching natural history data.

High-Fidelity Multiphysics Modeling of a Heat Pipe Microreactor Using BlueCrab

Researchers who are actively developing nuclear microreactors are planning to employ innovative designs and features using traditional commercial modeling tools that may be inadequate for their design and licensing activities. The codes developed under the U.S. Department of Energy Office of Nuclear Energy Advanced Modeling and Simulation (NEAMS) program provide flexibility in terms of geometry modeling and multiphysics coupling and are particularly well suited for modeling novel microreactor concepts. To test the maturity of these codes, this paper introduces a conceptual heat pipe microreactor (HP-MR) designed to gather various technologies of interest to microreactor developers such as control drums, heat pipes, and hydride moderators. The objective of this effort is to demonstrate NEAMS tools capability to perform high-fidelity multiphysics simulations, using coupled neutronics (via the Griffin code), heat conduction (via the BISON code), heat pipe modeling (via the Sockeye code), and hydrogen redistribution in hydride metal moderator (via the SWIFT code). Codes are coupled in-memory through the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, which permits flexible multiphysics data transfer schemes. The analysis confirmed two key aspects of the HP-MR concept: (1) its ability to follow the power load requested from the heat pipe and (2) its ability to avoid heat pipe cascading failure unless designed with high power close to operating failure limits of its heat pipes. The developed computational model was distributed publicly on the Virtual Test Bed for training purposes to accelerate adoption by industry and to provide a high-fidelity multiphysics solution for benchmarking against other tools. Additional multiphysics analyses including other transients and coupled physics were identified as necessary future work, together with a focus on validating multiphysics behavior against experiments.

Advanced analysis of single-molecule spectroscopic data

We present Full SMS, a multipurpose graphical user interface (GUI)-based software package for analyzing single-molecule spectroscopy (SMS) data. SMS typically delivers multiparameter data—such as fluorescence brightness, lifetime, and spectra—of molecular- or nanometer-scale particles such as single dye molecules, quantum dots, or fluorescently labeled biological macromolecules. Full SMS allows an unbiased statistical analysis of fluorescence brightness through level resolution and clustering, analysis of fluorescence lifetimes through decay fitting, as well as the calculation of second-order correlation functions and the display of fluorescence spectra and raster-scan images. Additional features include extensive data filtering options, a custom HDF5-based file format, and flexible data export options. The software is open source and written in Python but GUI based so it may be used without any programming knowledge. A multiprocess architecture was employed for computational efficiency. The software is also designed to be easily extendable to include additional import data types and analysis capabilities.

Testing behaviour change with an artificial intelligence chatbot in a randomized controlled study

Chatbots can effect large-scale behaviour change because they are accessible through social media, flexible, scalable, and gather data automatically. Yet research on the feasibility and effectiveness of chatbot-administered behaviour change interventions is sparse. The effectiveness of established behaviour change interventions when implemented in chatbots is not guaranteed, given the unique human–machine interaction dynamics. We pilot-tested chatbot-based behaviour change through information provision and embedded animations. We evaluated whether the chatbot could increase understanding and intentions to adopt protective behaviours during the pandemic. Fifty-nine culturally and linguistically diverse participants received a compassion intervention, an exponential growth intervention, or no intervention. We measured participants’ COVID-19 testing intentions and measured their staying-home attitudes before and after their chatbot interaction. We found reduced uncertainty about protective behaviours. The exponential growth intervention increased participants’ testing intentions. This study provides preliminary evidence that chatbots can spark behaviour change, with applications in diverse and underrepresented groups.

The Impact of Cloud Computing, Quantum Computing, and Blockchain on Data Security and Business Efficiency

This study explores the impact of cloud computing, quantum computing, and blockchain on data security and business efficiency. The primary objective is to qualitatively analyze the literature to understand how these emerging technologies contribute to enhancing security measures and operational efficiency within businesses. The research employs a qualitative literature review methodology, synthesizing findings from academic articles, industry reports, case studies, and empirical studies to provide a comprehensive overview of current knowledge in this field. The literature review methodology involves systematically collecting and analyzing scholarly sources that discuss various aspects of cloud computing, quantum computing, and blockchain. The study categorizes the literature into key themes, such as the benefits of cloud computing in providing scalable and flexible data storage solutions, the potential of quantum computing to revolutionize encryption and problem-solving capabilities, and the role of blockchain in ensuring transparency and immutability of data transactions. Thematic analysis is used to identify patterns and trends in how these technologies influence data security and business efficiency. The findings indicate that cloud computing enhances business efficiency by enabling seamless data access, reducing IT costs, and improving collaboration. Quantum computing holds promise for significantly advancing data security through quantum encryption, which offers higher levels of security compared to classical encryption methods. Blockchain technology ensures data integrity and transparency, reducing the risk of fraud and enhancing trust in digital transactions.

Verifiable and hybrid attribute-based proxy re-encryption for flexible data sharing in cloud storage

Cloud computing is a promising service architecture that enables a data owner to share data in an economic and efficient manner. To ensure data privacy, a data owner will generate the ciphertext of the data before outsourcing. Attribute-based encryption (ABE) provides an elegant solution for a data owner to enforce fine-grained access control on the data to be outsourced. However, ABE cannot support ciphertext transformation when needing to share the underlying data with a public-key infrastructure (PKI) user further. In addition, an untrusted cloud server may return random ciphertexts to the PKI user to save expensive computational costs of ciphertext transformation. To address above issues, we introduce a novel cryptographic primitive namely verifiable and hybrid attribute-based proxy re-encryption (VHABPRE). VHABPRE provides a transformation mechanism that re-encrypts an ABE ciphertext to a PKI-based public key encryption (PKE) ciphertext such that the PKI user can access the underlying data, meanwhile this PKI user can ensure the validity of the transformed ciphertext. By leveraging a key blinding technique and computing the commitment of the data, we construct two VHABPRE schemes to achieve flexible data sharing. We give formal security proofs and comprehensive performance evaluation to show the security and efficiency of the VHABPRE schemes.

Deduplication on Encrypted Data in Cloud Computing

Cloud storage is a crucial component of cloud computing, allowing users to expand their storage without upgrading their equipment and overcome resource constraints. Cloud users' data is always encrypted before being outsourced to ensure their security and privacy. However, encrypted data may result in a significant waste of cloud resources. Storage complicates data sharing among authorized users. We continue to face issues with encrypted data storage and deduplication. Traditional deduplication strategies are designed for certain application settings where data owners or cloud servers have full control over the process. They cannot meet data owners' varying requests based on data sensitivity. This study proposes a flexible data storage management method that combines deduplication and access control across various Clouds. Service Providers (CSP). We assess its performance through security analyses, comparisons, and implementations. The results demonstrate security, effectiveness, and efficiency for actual applications.

Studying K-12 and post-secondary teachers’ reflections on the value of a citizen science project for fostering learning: The case of a winter stream project

Citizen science programs offer the public opportunities to be involved in hands-on scientific data collection and study. Despite a history of incorporating citizen science in the K-12 classroom, little research has focused on projects that are appealing to both K-12 instructors and higher education faculty. Our study investigates initial barriers and motivations for participation in citizen science at multiple education levels using a winter, stream ecology citizen science project. Our findings indicate a growing interest in citizen science programs in winter, as they provide opportunities for collaboration, encourage flexible data collection, and offer tangible results for participants to reflect on their local ecological habitats.

Integrating OpenCV and Pandas for Enhanced Image Filtering and Color Detection

Image filtering and color detection in OpenCV is performed using functions like cv2.filter2D() for filtering, and cv2.cvtColor() for color space conversion and detection. These functions can be combined with other image processing techniques such as thresholding, erosion and dilation, and edge detection to perform more advanced tasks. The process typically starts with loading an image into Python using OpenCV's imread() function. From there, the image may be converted to a different color space using cvtColor() to make it easier to detect certain colors or features. Various image filters can then be applied to enhance the image and reduce noise or other unwanted features. This can include techniques such as blurring, sharpening, or edge detection, depending on the desired outcome. Once the image has been filtered, color detection can be performed using thresholding, which involves setting a threshold value and creating a binary image based on whether each pixel falls above or below the threshold. This can help to identify specific colors or features in the picture. Pandas are particularly useful for working with image data as it provides a flexible and efficient data structure called a DataFrame. This makes it easy to perform data manipulation tasks such as selecting, filtering, and grouping data, which can be useful for image processing tasks. Meanwhile, OpenCV provides a comprehensive set of tools for image filtering and color detection. These tools include a variety of filters, such as Gaussian, Median, and Bilateral filters, as well as thresholding techniques, such as binary and adaptive thresholding, which can be used to detect specific colors or features in an image. Keywords:- Image Processing, OpenCV, Color Detection, Image Filtering, cv2.filter2D, cv2.cvtColor, Thresholding, Erosion and Dilation, Edge Detection, Pandas DataFrame, Gaussian Filter, Median Filter, Bilateral Filter, Adaptive Thresholding, Feature Detection..

Modern approaches to data storage: comparison of relational and cloud data warehouses using etl and elt methods

The paper analyses various aspects of the use of relational and cloud data warehouses as well as methods of integrating ETL and ELT data. A comparative analysis of these approaches, their advantages and disadvantages are provided. A central relational data warehouse is proposed that provides a single version of truth (SVOT), which allows standardising and structuring data, avoiding differences and providing the access to the same information for all users of an organisation. It is analysed the methodological approaches to implementing a data warehouse: top-down, bottom-up, and from middle. It is described cloud data warehouses that use cloud technologies to provide scalability, availability and fault tolerance, which is important for the companies with huge amounts of data. The advantages and disadvantages of ETL and ELT are analysed: ETL transforms data before it is loaded into the warehouse, which makes it easier to maintain data confidentiality. ELT performs transformation after loading, which allows for more flexible data processing directly in the warehouse. In the article, we deal with the approaches to implementing a data warehouse: top-down is suitable for strategic planning, bottom-up allows for faster results, and the middle approach combines both methods to achieve optimal efficiency. We considered cloud data storage: compared to relational storage, cloud storage is more flexible, scalable and efficient, providing speed and reducing infrastructure costs. It is described cloud storage architectures: massive parallel processing, hybrid architectures, lambda architectures, and multi-structured architectures. They provide high performance and flexibility in data processing. It is described data storage technologies: Data Lake, Polyglot Persistence, Apache Iceberg, Apache Parquet, and columnar databases that provide efficient storage and processing of large amounts of data

Decentralized IoT data sharing: A blockchain-based federated learning approach with joint optimizations for efficiency and privacy

Blockchain-based Federated Learning (BCFL) is gaining significant attention as a promising decentralized data sharing technology with privacy protection. Most existing BCFL frameworks loosely couple blockchain and Federated Learning (FL). FL transforms data sharing into model sharing, while blockchain decentralizes the model aggregation and handles security verification. However, this simplistic overlay of the two technologies often involves third-party blockchain peers, leading to inefficient workflows and potential privacy attacks from malicious blockchain peers. Some studies have proposed differential privacy with noise addition to prevent privacy attacks, yet most do not allow clients to customize privacy budgets, impacting data availability and limiting BCFL’s application in data sharing. To address these challenges, we first introduce a novel tightly-coupled BCFL framework that integrates training and mining at the client side. Under this framework, a totally decentralized data sharing process is established. Moreover, a Personalised Differential Privacy (PDP) mechanism is devised, enabling clients to add Laplace noise to model gradients based on custom privacy budgets. To achieve optimal privacy budgets, a privacy optimization mechanism based on Stackelberg games is proposed. It establishes utility functions for data requesters and providers, models the process of utility optimization as a Stackelberg game process, and obtains the optimal privacy budget while maximizing utility for all participants. This facilitates flexible and on-demand privacy-protected data sharing. Extensive experiments validate the effectiveness of our approach in enhancing system efficiency and facilitating flexible on-demand privacy-protected data sharing, further solidifying BCFL’s potential in decentralized data sharing scenarios.

Multi-modal segmentation with missing image data for automatic delineation of gross tumor volumes in head and neck cancers.

Head and neck (HN) gross tumor volume (GTV) auto-segmentation is challenging due to the morphological complexity and low image contrast of targets. Multi-modality images, including computed tomography (CT) and positron emission tomography (PET), are used in the routine clinic to assist radiation oncologists for accurate GTV delineation. However, the availability of PET imaging may not always be guaranteed. To develop a deep learning segmentation framework for automated GTV delineation of HN cancers using a combination of PET/CT images, while addressing the challenge of missing PET data. Two datasets were included for this study: Dataset I: 524 (training) and 359 (testing) oropharyngeal cancer patients from different institutions with their PET/CT pairs provided by the HECKTOR Challenge; Dataset II: 90 HN patients(testing) from a local institution with their planning CT, PET/CT pairs. To handle potentially missing PET images, a model training strategy named the "Blank Channel" method was implemented. To simulate the absence of a PET image, a blank array with the same dimensions as the CT image was generated to meet the dual-channel input requirement of the deep learning model. During the model training process, the model was randomly presented with either a real PET/CT pair or a blank/CT pair. This allowed the model to learn the relationship between the CT image and the corresponding GTV delineation based on available modalities. As a result, our model had the ability to handle flexible inputs during prediction, making it suitable for cases where PET images are missing. To evaluate the performance of our proposed model, we trained it using training patients from Dataset I and tested it with Dataset II. We compared our model (Model 1) with two other models which were trained for specific modality segmentations: Model 2 trained with only CT images, and Model 3 trained with real PET/CT pairs. The performance of the models was evaluated using quantitative metrics, including Dice similarity coefficient (DSC), mean surface distance (MSD), and 95% Hausdorff Distance (HD95). In addition, we evaluated our Model 1 and Model 3 using the 359 test cases in Dataset I. Our proposed model(Model 1) achieved promising results for GTV auto-segmentation using PET/CT images, with the flexibility of missing PET images. Specifically, when assessed with only CT images in Dataset II, Model 1 achieved DSC of 0.56±0.16, MSD of 3.4±2.1mm, and HD95 of 13.9±7.6mm. When the PET images were included, the performance of our model was improved to DSC of 0.62±0.14, MSD of 2.8±1.7mm, and HD95 of 10.5±6.5mm. These results are comparable to those achieved by Model 2 and Model 3, illustrating Model 1's effectiveness in utilizing flexible input modalities. Further analysis using the test dataset from Dataset I showed that Model 1 achieved an average DSC of 0.77, surpassing the overall average DSC of 0.72 among all participants in the HECKTOR Challenge. We successfully refined a multi-modal segmentation tool for accurate GTV delineation for HN cancer. Our method addressed the issue of missing PET images by allowing flexible data input, thereby providing a practical solution for clinical settings where access to PET imaging may be limited.

A 3D imaging method for flexible array SAR based on a distributed POS

ABSTRACT The longer the baseline length of an airborne array synthetic aperture radar (SAR) is, the richer the ground object information can be resolved. The airborne array flexible SAR can overcome the limitation that the baseline length is limited by the belly length by attaching the antenna to the wing. However, fusing the airborne array flexible SAR distributed position and orientation system (POS) data under the wing and the belly is a major difficulty. To address this issue, the paper analyzes the inconsistency of airborne array flexible SAR data with a distributed POS and proposes a three-dimensional imaging method of airborne array flexible SAR based on distributed POS by fusing the Doppler centre, yaw angle and time of the observation data. By using distributed POS data, flexible data can obtain higher resolution than single-POS processing. The experiment of airborne array flexible SAR data proves the superiority of the proposed method.

Efficient and accountable anti-leakage attribute-based encryption scheme for cloud storage

To ensure secure and flexible data sharing in cloud storage, attribute-based encryption (ABE) is introduced to meet the requirements of fine-grained access control and secure one-to-many data sharing. However, the computational burden imposed by attribute encryption renders it unsuitable for resource-constrained environments such as the Internet of Things (IoT) and edge computing. Furthermore, the issue of accountability for illegal keys is crucial, as authorized users may actively disclose or sell authorization keys for personal gain, and keys may also passively leak due to management negligence or hacking incidents. Additionally, since all authorization keys are generated by the attribute authorization center, there is a potential risk of unauthorized key forgery. In response to these challenges, this paper proposes an efficient and accountable leakage-resistant scheme based on attribute encryption. The scheme adopts more secure online/offline encryption mechanisms and cloud server-assisted decryption to alleviate the computational burden on resource-constrained devices. For illegal keys, the scheme supports accountability for both users and the authorization center, allowing the revocation of decryption privileges for malicious users. In the case of passively leaked keys, timely key updates and revocation of decryption capabilities for leaked keys are implemented. Finally, the paper provides selective security and accountability proofs for the scheme under standard models. Efficiency analysis and experimental results demonstrate that the proposed scheme enhances encryption/decryption efficiency, and the storage overhead for accountability is also extremely low.

A Flexible and Accurate Additive Manufacturing Data Retrieval Method Based on Probabilistic Modeling and Transformation-Invariant Feature Learning

A Flexible and Accurate Additive Manufacturing Data Retrieval Method Based on Probabilistic Modeling and Transformation-Invariant Feature Learning

Multi-Attack Intrusion Detection for In-Vehicle CAN-FD Messages.

As an enhanced version of standard CAN, the Controller Area Network with Flexible Data (CAN-FD) rate is vulnerable to attacks due to its lack of information security measures. However, although anomaly detection is an effective method to prevent attacks, the accuracy of detection needs further improvement. In this paper, we propose a novel intrusion detection model for the CAN-FD bus, comprising two sub-models: Anomaly Data Detection Model (ADDM) for spotting anomalies and Anomaly Classification Detection Model (ACDM) for identifying and classifying anomaly types. ADDM employs Long Short-Term Memory (LSTM) layers to capture the long-range dependencies and temporal patterns within CAN-FD frame data, thus identifying frames that deviate from established norms. ACDM is enhanced with the attention mechanism that weights LSTM outputs, further improving the identification of sequence-based relationships and facilitating multi-attack classification. The method is evaluated on two datasets: a real-vehicle dataset including frames designed by us based on known attack patterns, and the CAN-FD Intrusion Dataset, developed by the Hacking and Countermeasure Research Lab. Our method offers broader applicability and more refined classification in anomaly detection. Compared with existing advanced LSTM-based and CNN-LSTM-based methods, our method exhibits superior performance in detection, achieving an improvement in accuracy of 1.44% and 1.01%, respectively.

Conformal, stretchable, breathable, wireless epidermal surface electromyography sensor system for hand gesture recognition and rehabilitation of stroke hand function

Surface electromyography (sEMG) plays a significant role in the everyday practice of clinic hand function rehabilitation. The materials and design of current typical clinic sEMG electrodes are rigid Ag/AgCl or flexible polyimide (PI) film, which cannot provide a stable interface between electrodes and skin for adequate long-term high-quality data. Thus, conformal, soft, breathable, wireless epidermal sEMG sensor systems have broad potential relevance to clinic rehabilitation settings. Herein, we demonstrate a stretchable epidermal sEMG sensor array system with optimized materials and structure strategies for hand gesture recognition and hand function rehabilitation. The optimized serpentine structures with marvelous stretchability and improved fill ratio, provide lower impedance and high-quality sEMG signals. Moreover, the easy-to-use airhole method further ensures stable and long-term contact with the skin for recording. In addition, integrated with a customized flexible wireless data acquisition system, the capability for real-time 8-channel sEMG monitoring is developed, and taking together with the CNN-based algorithm, the system can automatically and reliably realize the 7 kinds of hand gestures with an accuracy of 81.02%. Moreover, the low-cost yet high-performance epidermal sEMG sensor system demonstrated its conceptual feasibility in quantitatively evaluation of stroke patient’s hand and facilitating human-robot collaboration in hand rehabilitation by proof-of-the-concept clinical testing.