4.6 Article

Semi-Supervised Adversarial Auto-Encoder to Expedite Human Activity Recognition

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

Impact of Wireless Sensor Data Mining with Hybrid Deep Learning for Human Activity Recognition

Rajit Nair et al.

Summary: This study demonstrates the use of a hybrid deep learning model for human activity recognition, achieving superior accuracy compared to other state-of-the-art algorithms in this field through the training and evaluation of wireless sensor data.

WIRELESS COMMUNICATIONS & MOBILE COMPUTING (2022)

Article Computer Science, Artificial Intelligence

Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding

Mathew Monfort et al.

Summary: Videos often contain multiple sequential and simultaneous actions, but most datasets only provide a single label per video. To address this limitation, a multi-label dataset is introduced for training and analyzing models for multi-action detection. The baseline results for multi-action recognition using adapted loss functions and improved visualization methods are presented, demonstrating the advantages of transferring trained models to smaller datasets.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2022)

Article Chemistry, Analytical

Semi-Supervised Adversarial Learning Using LSTM for Human Activity Recognition

Sung-Hyun Yang et al.

Summary: This paper proposes a semi-supervised adversarial learning approach using LSTM for human activity recognition. The method improves the learning capabilities in dealing with errors and adapts to changes in human activity routines and new activities without prior understanding and historical information.

SENSORS (2022)

Article Chemistry, Analytical

Complex Deep Neural Networks from Large Scale Virtual IMU Data for Effective Human Activity Recognition Using Wearables

Hyeokhyen Kwon et al.

Summary: The study demonstrates the training of more complex HAR systems using large-scale virtual IMU datasets, where the model captures key points of IMU data for training and achieves significant performance improvements.

SENSORS (2021)

Article Chemistry, Analytical

Study on Human Activity Recognition Using Semi-Supervised Active Transfer Learning

Seungmin Oh et al.

Summary: In recent years, deep learning models have been used for research in human activity recognition, but the lack of labeled data has led to slow development. Existing methods heavily rely on manual data collection and labeling, resulting in slow and biased processes. By proposing a solution using semi-supervised active transfer learning to reduce labeling tasks, performance was improved while reducing the amount of labeling required.

SENSORS (2021)

Article Computer Science, Hardware & Architecture

Deep Learning Models for Real-time Human Activity Recognition with Smartphones

Shaohua Wan et al.

MOBILE NETWORKS & APPLICATIONS (2020)

Article Computer Science, Artificial Intelligence

Discovery and Recognition of Emerging Human Activities Using a Hierarchical Mixture of Directional Statistical Models

Lei Fang et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2020)

Proceedings Paper Computer Science, Cybernetics

Semi-supervised Learning for Human Activity Recognition Using Adversarial Autoencoders

Dmitrijs Balabka

UBICOMP/ISWC'19 ADJUNCT: PROCEEDINGS OF THE 2019 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING AND PROCEEDINGS OF THE 2019 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS (2019)

Article Computer Science, Information Systems

Lifelong Learning in Sensor-Based Human Activity Recognition

Juan Ye et al.

IEEE PERVASIVE COMPUTING (2019)

Article Automation & Control Systems

Feasibility of Wrist-Worn, Real-Time Hand, and Surface Gesture Recognition via sEMG and IMU Sensing

Shuo Jiang et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2018)

Article Computer Science, Artificial Intelligence

Real-time human activity recognition from accelerometer data using Convolutional Neural Networks

Andrey Ignatov

APPLIED SOFT COMPUTING (2018)

Review Computer Science, Artificial Intelligence

Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges

Henry Friday Nweke et al.

EXPERT SYSTEMS WITH APPLICATIONS (2018)

Article Engineering, Electrical & Electronic

Device-Free Human Activity Recognition Using Commercial WiFi Devices

Wei Wang et al.

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS (2017)

Article Computer Science, Information Systems

Two-Layer Hidden Markov Model for Human Activity Recognition in Home Environments

M. Humayun Kabir et al.

INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS (2016)

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)

Article Computer Science, Artificial Intelligence

Transfer learning for activity recognition: a survey

Diane Cook et al.

KNOWLEDGE AND INFORMATION SYSTEMS (2013)

Article Sport Sciences

Activity Recognition Using a Single Accelerometer Placed at the Wrist or Ankle

Andrea Mannini et al.

MEDICINE & SCIENCE IN SPORTS & EXERCISE (2013)

Article Computer Science, Software Engineering

Multimodal Recognition of Reading Activity in Transit Using Body-Worn Sensors

Andreas Bulling et al.

ACM TRANSACTIONS ON APPLIED PERCEPTION (2012)

Article Computer Science, Artificial Intelligence

Domain Transfer Multiple Kernel Learning

Lixin Duan et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2012)

Article Computer Science, Information Systems

An activity monitoring system for elderly care using generative and discriminative models

T. L. M. van Kasteren et al.

PERSONAL AND UBIQUITOUS COMPUTING (2010)