☆ 4.7 Article

Understanding and Improving Channel Attention for Human Activity Recognition by Temporal-Aware and Modality-Aware Embedding

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Computer Science, Information Systems

Binarized Neural Network for Edge Intelligence of Sensor-Based Human Activity Recognition

Fei Luo et al.

Summary: A wide range of sensors is used in human activity recognition, generating large amounts of data during monitoring. Server-based and cloud-based computing require uploading all sensor data, leading to increased costs and latency. However, the development of edge computing addresses this problem by moving computation and data storage closer to the sensors instead of relying on central servers/clouds.

IEEE TRANSACTIONS ON MOBILE COMPUTING (2023)

添加到收藏夹

Article Computer Science, Software Engineering

A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data

Sravan Kumar Challa et al.

Summary: This paper proposes a hybrid CNN-BiLSTM network for HAR, which automatically extracts features from raw sensor data and allows the model to learn local features and long-term dependencies. Different filter sizes capture various temporal local dependencies to improve the feature extraction process. Experiment results show that the proposed model outperforms other methods with high accuracies on benchmark datasets.

VISUAL COMPUTER (2022)

添加到收藏夹

Article Multidisciplinary Sciences

A Novel CNN-based Bi-LSTM parallel model with attention mechanism for human activity recognition with noisy data

Xiaochun Yin et al.

Summary: This paper proposes a parallel model based on one-dimensional convolutional neural network (CNN) and bidirectional LSTM, with an attention mechanism, for human activity recognition that deals with long-time dependent sequence samples with noisy and missed data. Evaluation on public datasets demonstrates that the model achieves excellent performance in terms of classification accuracy and computational time complexity.

SCIENTIFIC REPORTS (2022)

添加到收藏夹

Article Computer Science, Information Systems

Ensembled Transfer Learning Based Multichannel Attention Networks for Human Activity Recognition in Still Images

Koki Hirooka et al.

Summary: In this paper, a convolutional neural network architecture with attention networks was proposed for human activity recognition. Experimental results showed that the proposed architecture outperformed previous methods.

IEEE ACCESS (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Triple Cross-Domain Attention on Human Activity Recognition Using Wearable Sensors

Yin Tang et al.

Summary: Efficiently identifying activities of daily living (ADL) is crucial for improving sports tracking and healthcare applications. Existing attention mechanisms in sensor-based human activity recognition (HAR) have achieved remarkable performance, but they overlook the cross-interaction between different dimensions. This paper proposes a triplet cross-dimension attention method that captures the interaction between sensor dimension, temporal dimension, and channel dimension, leading to improved classification performance.

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

DanHAR: Dual Attention Network for multimodal human activity recognition using wearable sensors

Wenbin Gao et al.

Summary: The paper introduces a new dual attention method called DanHAR, which combines channel and temporal attention on residual networks to enhance feature representation for sensor-based HAR tasks. Experimental results on multiple datasets demonstrate the central importance of dual attention mechanism in activity recognition tasks, showing performance improvement over other algorithms. Visualizing analysis confirms the ability of the proposed attention mechanism to capture spatial-temporal dependencies in multimodal sensing data.

APPLIED SOFT COMPUTING (2021)

添加到收藏夹

Article Engineering, Electrical & Electronic

Smartphone Sensor-Based Human Activity Recognition Robust to Different Sampling Rates

Tatsuhito Hasegawa

Summary: The study proposed an activity recognition method robust to different sampling rates by training a predictive model on sensor values to acquire accurate feature representations in various measurement environments, leading to improved estimation accuracy in machine learning.

IEEE SENSORS JOURNAL (2021)

添加到收藏夹

Article Computer Science, Theory & Methods

Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges, and Opportunities

Kaixuan Chen et al.

Summary: This study explores the impact of sensor devices and the Internet of Things on sensor-based activity recognition, discusses the role of deep learning methods in addressing recognition challenges, and provides an overview of current research progress and future directions.

ACM COMPUTING SURVEYS (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Sequential Weakly Labeled Multiactivity Localization and Recognition on Wearable Sensors Using Recurrent Attention Networks

Kun Wang et al.

Summary: The article introduces a recurrent attention network for handling sequential weakly labeled multiactivity recognition and location tasks, effectively inferring multiple activity types and determining specific locations of each target activity, as well as reducing the burden of manual labeling.

IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS (2021)

添加到收藏夹

Article Engineering, Electrical & Electronic

Learning Disentangled Representation for Mixed-Reality Human Activity Recognition With a Single IMU Sensor

Songpengcheng Xia et al.

Summary: This article introduces a novel deep learning method for accurate and robust human activity recognition with only using a single IMU sensor. By constructing a large synthetic dataset with multimodal measurements and proposing a multiple-level domain adaptive learning model, the neural network is encouraged to learn a disentangled representation for multimodal sensing data, achieving favorable performance in comparison with competing methods.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

添加到收藏夹

Article Engineering, Electrical & Electronic

Deep Neural Networks for Sensor-Based Human Activity Recognition Using Selective Kernel Convolution

Wenbin Gao et al.

Summary: This article introduces a new multibranch CNN that uses a selective kernel mechanism for HAR, adopting an attention idea to perform kernel selection among multiple branches, achieving higher recognition accuracy.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

添加到收藏夹

Article Computer Science, Information Systems

On-Device Lumbar-Pelvic Movement Detection Using Dual-IMU: A DNN-Based Approach

Yuxin Zhang et al.

Summary: This study proposes a novel approach for lumbar-pelvic movements detection using deep learning and local detection on mobile devices to enhance real-time feedback and therapy. The research addresses existing limitations and improves detection accuracy by introducing domain-specific rules based on transition logic.

IEEE ACCESS (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Evaluation of artificial intelligence techniques for the classification of different activities of daily living and falls

Ivanoe De Falco et al.

NEURAL COMPUTING & APPLICATIONS (2020)

添加到收藏夹

Article Engineering, Electrical & Electronic

Smartphone Sensor-Based Human Activity Recognition Using Feature Fusion and Maximum Full a Posteriori

Zhenghua Chen et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)

添加到收藏夹

Article Engineering, Electrical & Electronic

An Automated Daily Sports Activities and Gender Recognition Method Based on Novel Multikernel Local Diamond Pattern Using Sensor Signals

Turker Tuncer et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)

添加到收藏夹

Article Automation & Control Systems

Interpretable and Accurate Convolutional Neural Networks for Human Activity Recognition

Eunji Kim

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Deep learning for sensor-based activity recognition: A survey

Jindong Wang et al.

PATTERN RECOGNITION LETTERS (2019)

添加到收藏夹

Article Computer Science, Theory & Methods

A Survey on Deep Learning: Algorithms, Techniques, and Applications

Samira Pouyanfar et al.

ACM COMPUTING SURVEYS (2019)

添加到收藏夹

Article Engineering, Electrical & Electronic

Weakly Supervised Human Activity Recognition From Wearable Sensors by Recurrent Attention Learning

Jun He et al.

IEEE SENSORS JOURNAL (2019)

添加到收藏夹

Article Engineering, Electrical & Electronic

Attention-Based Convolutional Neural Network for Weakly Labeled Human Activities' Recognition With Wearable Sensors

Kun Wang et al.

IEEE SENSORS JOURNAL (2019)

添加到收藏夹

Article Computer Science, Information Systems

Asymmetric Residual Neural Network for Accurate Human Activity Recognition

Jun Long et al.

INFORMATION (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A novel random forests based class incremental learning method for activity recognition

Chunyu Hu et al.

PATTERN RECOGNITION (2018)

添加到收藏夹

Article Chemistry, Analytical

Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors

Frederic Li et al.

SENSORS (2018)

添加到收藏夹

Proceedings Paper Computer Science, Hardware & Architecture

Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention

Ming Zeng et al.

ISWC'18: PROCEEDINGS OF THE 2018 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS (2018)

添加到收藏夹

Proceedings Paper Computer Science, Hardware & Architecture

On Attention Models for Human Activity Recognition

Vishvak S. Murahari et al.

ISWC'18: PROCEEDINGS OF THE 2018 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS (2018)

添加到收藏夹

Article Chemistry, Multidisciplinary

UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

Daniela Micucci et al.

APPLIED SCIENCES-BASEL (2017)

添加到收藏夹

Proceedings Paper Computer Science, Cybernetics

CNN-based Sensor Fusion Techniques for Multimodal Human Activity Recognition

Sebastian Muenzner et al.

PROCEEDINGS OF THE 2017 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS (ISWC 17) (2017)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Transition-Aware Human Activity Recognition Using Smartphones

Jorge-L. Reyes-Ortiz et al.

NEUROCOMPUTING (2016)

添加到收藏夹

Article Chemistry, Analytical

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

Francisco Javier Ordonez et al.

SENSORS (2016)

添加到收藏夹

Article Engineering, Electrical & Electronic

Bearing Health Monitoring Based on Hilbert-Huang Transform, Support Vector Machine, and Regression

Abdenour Soualhi et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2015)

添加到收藏夹

Article Computer Science, Theory & Methods

A Tutorial on Human Activity Recognition Using Body-Worn Inertial Sensors

Andreas Bulling et al.

ACM COMPUTING SURVEYS (2014)

添加到收藏夹

Article Computer Science, Information Systems

A Survey on Human Activity Recognition using Wearable Sensors

Oscar D. Lara et al.

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2013)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.