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Deep Learning-Enhanced Internet of Things for Activity Recognition in Post-Stroke Rehabilitation

ieee JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第7期28卷 3851-3859页

作者： Jin, Fangpeng Zou, Mi Peng, Xiaoyun Lei, Hua Ren, Yongjun Nanjing Univ Informat Sci & Technol Sch Comp Nanjing 210044 Peoples R China Sichuan Acad Med Sci Hosp Univ Elect Sci & Technol China Univ Elect Sci & Technol China Sichuan Prov Peoples HospDept RehabilSch Med Chengdu 610072 Peoples R China

Wearable sensors provide a more effective means of activity monitoring and management by recording patients' daily activity data for assessing their daily function and rehabilitation progress, as well as providing a convenient and practical solution for human activity recognition (HAR). However, during the motor rehabilitation of stroke patients, sensors provide vast amounts of high-dimensional data that are large and complex. To enhance the accuracy of activity monitoring and identification, as well as address the limitations of real-time processing, data visualization, and tracking in conventional monitoring approaches, it is essential to perform valid data processing and analysis. This paper combines deep learning models to explore the potential relationships and patterns between data to build an intelligent post-stroke rehabilitation system. This paper proposes a novel framework aimed at accurately recognizing activities performed by stroke patients. Our approach leverages a data fusion mechanism based on multiple sensors to construct a fusion tensor and employs a bidirectional long and short-term memory (BiLSTM) network enhanced with an attention mechanism. This network effectively captures temporal patterns and long-term dependencies within the data, resulting in improved performance for wearable sensor-based activity classification. Furthermore, we introduce an enhanced loss function to optimize the learning process. To assess the performance of the proposed model algorithm, two benchmark datasets were employed. These datasets served as the basis for evaluating and comparing the baseline method as well as other proposed methods. The experimental results clearly demonstrated that the proposed model outperformed the compared methods, indicating its superior performance in activity recognition.

关键词： Sensors Wearable sensors Stroke (medical condition) data models Monitoring Feature extraction Deep learning stroke activity recognition deep learning Internet of Things

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ieee LDAV 2024 Preface

IEEE LDAV 2024 Preface

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ieee symposium on large data analysis and visualization (LDAV)

作者： Johanna Beyer Steffen Frey Guido Reina Silvio Rizzi Gunther H. Weber Soumya Dutta Nicole Marsaglia Peer-Timo Bremer Kenneth Moreland Kristi Potter Julien Tierny Chaoli Wang Hongfeng Yu Harvard University University of Groningen University of Stuttgart Argonne National Laboratory Lawrence Berkeley National Laboratory Indian Institute of Technology Kanpur (IITK) Lawrence Livermore National Laboratory Oak Ridge National Laboratory National Renewable Energy Laboratory CNRS - Sorbonne Université University of Notre Dame University of Nebraska-Lincoln

Join us for the 14th ieee symposium on large data analysis and visualization (ieee LDAV) on Sunday, October 13th 2024 collocated with ieee VIS 2024 in St. Pete Beach, Florida, USA.

ISBN: (数字)9798331516925

ISBN: (纸本)9798331516932

Join us for the 14th ieee symposium on large data analysis and visualization (ieee LDAV) on Sunday, October 13th 2024 collocated with ieee VIS 2024 in St. Pete Beach, Florida, USA.

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Reliability analysis of Phase Change Memory-Based Neuromorphic Circuits

Reliability Analysis of Phase Change Memory-Based Neuromorph...

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ieee-Computer-Society Annual symposium on VLSI (ISVLSI)

作者： Titirsha, Twisha Shuvo, Md Maruf Hossain Islam, Syed Kamrul Univ Missouri Dept Elect Engn & Comp Sci Columbia MO 65211 USA Univ Texas El Paso Dept Elect & Comp Engn El Paso TX 79968 USA

ISBN: (纸本)9798350354119

This paper presents an in-depth reliability analysis of neuromorphic circuits utilizing phase change memory (PCM). In-memory computing with PCM cells to store synaptic weights is exploited to address the performance gap between processing needs and memory constraints in machine learning computation. The integration of PCM for hardware realization of spiking neural networks (SNNs) enhances data processing and transmission capabilities. However, challenges remain in memristor-based crossbar architectures, including the effects of voltage drops and current fluctuations on the reliability of PCM. Hence, a reliability model accounting for process variations and temperature influences is proposed. The model effectively predicts the reliability of PCM in neuromorphic circuits, paving the way for more robust cognitive computing applications.

关键词： crossbar architecture memristor neuromorphic circuit phase change memory reliability

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Embedding Environmental Intelligence in Low-cost Drones

Embedding Environmental Intelligence in Low-cost Drones

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ieee-Computer-Society Annual symposium on VLSI (ISVLSI)

作者： Gonzalez, Roberto Gomez Gonzalez, Miguel Gomez Trevino, Luis Sundaravadivel, Prabha Univ Texas Tyler Dept Elect & Comp Engn Tyler TX 75701 USA

ISBN: (纸本)9798350354119

Effective environmental monitoring is key in our rapidly changing world, where environmental metrics such as temperature, humidity, and air quality can unexpectedly fluctuate and cause health risks and ecological imbalances and alter informed policymaking. Traditional monitoring paradigms, often associated with latency and high costs, are becoming progressively inadequate for real-time data analysis and decision-making demands. Addressing this critical gap, this paper unveils a cost-effective option for leveraging Aerial Vehicles. The Tello EDU drone, integrated with highly accurate sensors and interfaced with a Raspberry Pi 4B for advanced data processing, brings to life a potential replacement for current environmental monitoring systems. The paper delineates a comprehensive system architecture that synergizes airborne sensing with ground-level data synthesis, enabling instantaneous environmental analytics. Our approach focuses on a new era of community-centric and governance-responsive environmental surveillance. Preliminary results are promising, showcasing the proposed system's capability to transcend the limitations of existing methodologies by delivering a more efficient and scalable solution for environmental oversight.

关键词： Environmental Monitoring Unmanned Aerial Vehicles (UAVs) Internet of Things (IoT) Real-time data Processing Air Quality Sensors Raspberry Pi Influx DB

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JIST: Joint Image and Sequence Training for Sequential Visual Place Recognition

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ieee ROBOTICS AND AUTOMATION LETTERS 2024年第2期9卷 1310-1317页

作者： Berton, Gabriele Trivigno, Gabriele Caputo, Barbara Masone, Carlo Politecn Torino Dept Control & Comp Engn Visual & Multimodal Appl Learning Lab I-10138 Turin Italy

Visual Place Recognition aims at recognizing previously visited places by relying on visual clues, and it is used in robotics applications for SLAM and localization. Since typically a mobile robot has access to a continuous stream of frames, this task is naturally cast as a sequence-to-sequence localization problem. Nevertheless, obtaining sequences of labelled data is much more expensive than collecting isolated images, which can be done in an automated way with little supervision. As a mitigation to this problem, we propose a novel Joint Image and Sequence Training (JIST) protocol that leverages large uncurated sets of images through a multi-task learning framework. With JIST we also introduce SeqGeM, an aggregation layer that revisits the popular GeM pooling to produce a single robust and compact embedding from a sequence of single-frame embeddings. We show that our model is able to outperform previous state of the art while being faster, using eight times smaller descriptors, having a lighter architecture and allowing to process sequences of various lengths.

关键词： Training Task analysis visualization databases Multitasking Streaming media Location awareness Representation learning simultaneous localization and mapping visual information retrieval

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Towards an Integrated Performance Framework for Fire Science and Management Workflows 24

Towards an Integrated Performance Framework for Fire Science...

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24th ieee/ACM International symposium on Cluster, Cloud, and Internet Computing (CCGrid)

作者： Ahmed, Hena Shende, Ravi Perez, Ismael Crawl, Daniel Purawat, Shweta Altintas, Ilkay Univ Calif San Diego Halicioglu Data Sci Inst La Jolla CA 92093 USA Univ Calif San Diego San Diego Supetromputer Ctr La Jolla CA 92093 USA

ISBN: (纸本)9798350377521;9798350377514

Reliable performance metrics are necessary prerequisites to building large-scale end-to-end integrated workflows for collaborative scientific research, particularly within context of use-inspired decision making platforms with many concurrent users and when computing real-time and urgent results using large data. This work is a building block for the National data Platform, which leverages multiple use-cases including the WIFIRE data and Model Commons for wildfire behavior modeling and the EarthScope Consortium for collaborative geophysical research. This paper presents an artificial intelligence and machine learning (AI/ML) approach to performance assessment and optimization of scientific workflows. An associated early AI/ML framework spanning performance data collection, prediction and optimization is applied to wildfire science applications within the WIFIRE BurnPro3D (BP3D) platform for proactive fire management and mitigation.

关键词： Cyberinfrastructure Workflows Performance analysis Artificial Intelligence Machine Learning

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SRRNet: A Transformer Structure With Adaptive 2-D Spatial Attention Mechanism for Cell Phone-Captured Shopping Receipt Recognition

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ieee TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 3289-3298页

作者： Zhou, Haibin Shao, Lujiao Zhang, Haijun Harbin Inst Technol Shenzhen Dept Comp Sci Shenzhen 518055 Peoples R China

Shopping receipts, which are regarded as a kind of consumption proof provided to consumers, contain important information for trade. The digitalization of shopping receipts by extracting text information from images can provide smart retail with precise data analysis for commodity management and supply chain optimization. Despite the fact that traditional optical character recognition (OCR) systems have performed well on document template-based recognition, accurate recognition of receipts taken by cell-phones remains difficult due to the uncertainty of the shooting environment. To address irregular text recognition for receipts, in this research we propose a transformer-based text recognition network model by developing an adaptive 2D spatial attention module to extract the 2D correlation information of image features. We examined the performance of our proposed model on both public benchmarks and a large-scale real-world shopping receipt text recognition dataset. Results demonstrate the efficacy of the proposed method in comparison to extant approaches.

关键词： Text recognition Transformers Decoding Feature extraction Optical character recognition visualization Adaptation models Shopping receipt optical character recognition text recognition spatial attention transformer

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Open Long-Tailed Recognition in a Dynamic World

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ieee TRANSACTIONS ON PATTERN analysis AND MACHINE INTELLIGENCE 2024年第3期46卷 1836-1851页

作者： Liu, Ziwei Miao, Zhongqi Zhan, Xiaohang Wang, Jiayun Gong, Boqing Yu, Stella X. Nanyang Technol Univ Singapore 639798 Singapore Univ Calif Berkeley Int Comp Sci Inst Berkeley CA 94720 USA Chinese Univ Hong Kong Hong Kong Peoples R China Google Inc Mountain View CA 94043 USA

Real world data often exhibits a long-tailed and open-ended (i.e., with unseen classes) distribution. A practical recognition system must balance between majority (head) and minority (tail) classes, generalize across the distribution, and acknowledge novelty upon the instances of unseen classes (open classes). We define Open Long-Tailed Recognition++ (OLTR++) as learning from such naturally distributed data and optimizing for the classification accuracy over a balanced test set which includes both known and open classes. OLTR++ handles imbalanced classification, few-shot learning, open-set recognition, and active learning in one integrated algorithm, whereas existing classification approaches often focus only on one or two aspects and deliver poorly over the entire spectrum. The key challenges are: 1) how to share visual knowledge between head and tail classes, 2) how to reduce confusion between tail and open classes, and 3) how to actively explore open classes with learned knowledge. Our algorithm, OLTR++, maps images to a feature space such that visual concepts can relate to each other through a memory association mechanism and a learned metric (dynamic meta-embedding) that both respects the closed world classification of seen classes and acknowledges the novelty of open classes. Additionally, we propose an active learning scheme based on visual memory, which learns to recognize open classes in a data-efficient manner for future expansions. On three large-scale open long-tailed datasets we curated from ImageNet (object-centric), Places (scene-centric), and MS1M (face-centric) data, as well as three standard benchmarks (CIFAR-10-LT, CIFAR-100-LT, and iNaturalist-18), our approach, as a unified framework, consistently demonstrates competitive performance. Notably, our approach also shows strong potential for the active exploration of open classes and the fairness analysis of minority groups.

关键词： Tail visualization Head Training Task analysis Measurement Magnetic heads Long-tailed recognition few-shot learning active learning

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A Multimodal Framework for Depression Detection During COVID-19 via Harvesting Social Media

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ieee TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第2期11卷 2872-2888页

作者： Anshul, Ashutosh Pranav, Gumpili Sai Rehman, Mohammad Zia Ur Kumar, Nagendra Indian Inst Technol Indore Indore 453552 India

The recent coronavirus disease (COVID-19) has become a pandemic and has affected the entire globe. During the pandemic, we have observed a spike in cases related to mental health, such as anxiety, stress, and depression. Depression significantly influences most diseases worldwide, making it difficult to detect mental health conditions in people due to unawareness and unwillingness to consult a doctor. However, nowadays, people extensively use online social media platforms to express their emotions and thoughts. Hence, social media platforms are now becoming a large data source that can be utilized for detecting depression and mental illness. However, the existing approaches often overlook data sparsity in tweets and the multimodal aspects of social media. In this article, we propose a novel multimodal framework that combines textual, user-specific, and image analysis to detect depression among social media users. To provide enough context about the user's emotional state, we propose the following: 1) an extrinsic feature by harnessing the URLs present in tweets and 2) extracting textual content present in images posted in tweets. We also extract five sets of features belonging to different modalities to describe a user. In addition, we introduce a deep learning model, the visual neural network (VNN), to generate embeddings of user-posted images, which are used to create the visual feature vector for prediction. We contribute a curated COVID-19 dataset of depressed and nondepressed users for research purposes and demonstrate the effectiveness of our model in detecting depression during the COVID-19 outbreak. Our model outperforms the existing state-of-the-art methods over a benchmark dataset by 2%-8% and produces promising results on the COVID-19 dataset. Our analysis highlights the impact of each modality and provides valuable insights into users' mental and emotional states.

关键词： Depression Feature extraction Social networking (online) COVID-19 Blogs visualization Surveys Coronavirus disease (COVID-19) deep learning depression machine learning multimodal analysis social media

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Efficient representation and analysis for a large tetrahedral mesh using Apache Spark

Efficient representation and analysis for a large tetrahedra...

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2024 ieee Topological data analysis and visualization, TopoInVis 2024

作者： Qian, Yuehui Liu, Guoxi Iuricich, Federico Floriani, Leila De University of Maryland College Park United States Clemson University United States

ISBN: (纸本)9798331528447

Tetrahedral meshes are widely used due to their flexibility and adaptability in representing changes of complex geometries and topology. However, most existing data structures struggle to efficiently encode the irregular connectivity of tetrahedral meshes with billions of vertices. We address this problem by proposing a novel framework for efficient and scalable analysis of large tetrahedral meshes using Apache Spark. The proposed framework, called Tetra-Spark, features optimized approaches to locally compute many connectivity relations by first retrieving the Vertex-Tetrahedron (VT) relation. This strategy significantly improves Tetra-Spark's efficiency in performing morphology computations on large tetrahedral meshes. To prove the effectiveness and scalability of such a framework, we conduct a comprehensive comparison against a vanilla Spark implementation for the analysis of tetrahedral meshes. Our experimental evaluation shows that Tetra-Spark achieves up to a 78 × speedup and reduces memory usage by up to 80% when retrieving connectivity relations with the VT relation available. This optimized design further accelerates subsequent morphology computations, resulting in up to a 47.7 × speedup. © 2024 ieee.

关键词： Mesh generation

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