ArticleSensors (Basel, Switzerland)2019
Fusion of Video and Inertial Sensing for Deep Learning-Based Human Action Recognition.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Deep Learning for Sensor-Based Sport Performance and Health Monitoring: A Review of Wearable, Vision-Based, and Multimodal Sensing Approaches.Sensors (Basel, Switzerland) · 2026Review
- Intelligent ADL Recognition via IoT-Based Multimodal Deep Learning Framework.Sensors (Basel, Switzerland) · 2023Article
- Identifying the "Dangshan" Physiological Disease of Pear Woolliness Response via Feature-Level Fusion of Near-Infrared Spectroscopy and Visual RGB Image.Foods (Basel, Switzerland) · 2023Article
- Sports Action Recognition Based on Deep Learning and Clustering Extraction Algorithm.Computational intelligence and neuroscience · 2022Article
- Lite-3DCNN Combined with Attention Mechanism for Complex Human Movement Recognition.Computational intelligence and neuroscience · 2022Article
- Robust Human Activity Recognition by Integrating Image and Accelerometer Sensor Data Using Deep Fusion Network.Sensors (Basel, Switzerland) · 2021Article
- Malicious Network Behavior Detection Using Fusion of Packet Captures Files and Business Feature Data.Sensors (Basel, Switzerland) · 2021Article
- Development and Validation of a 3-Dimensional Convolutional Neural Network for Automatic Surgical Skill Assessment Based on Spatiotemporal Video Analysis.JAMA network open · 2021Article
- Activity Recognition for Ambient Assisted Living with Videos, Inertial Units and Ambient Sensors.Sensors (Basel, Switzerland) · 2021Article
- A Hierarchical Learning Approach for Human Action Recognition.Sensors (Basel, Switzerland) · 2020Article
- Real-Time Abnormal Event Detection for Enhanced Security in Autonomous Shuttles Mobility Infrastructures.Sensors (Basel, Switzerland) · 2020Article
- Deep Learning-Based Real-Time Multiple-Person Action Recognition System.Sensors (Basel, Switzerland) · 2020Article
- Spatiotemporal Interaction Residual Networks with Pseudo3D for Video Action Recognition.Sensors (Basel, Switzerland) · 2020Article
- C-MHAD: Continuous Multimodal Human Action Dataset of Simultaneous Video and Inertial Sensing.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
This paper presents the simultaneous utilization of video images and inertial signals that are captured at the same time via a video camera and a wearable inertial sensor within a fusion framework in order to achieve a more robust human action recognition compared to the situations when each sensing modality is used individually. The data captured by these sensors are turned into 3D video images and 2D inertial images that are then fed as inputs into a 3D convolutional neural network and a 2D convolutional neural network, respectively, for recognizing actions. Two types of fusion are considered-Decision-level fusion and feature-level fusion. Experiments are conducted using the publicly available dataset UTD-MHAD in which simultaneous video images and inertial signals are captured for a total of 27 actions. The results obtained indicate that both the decision-level and feature-level fusion approaches generate higher recognition accuracies compared to the approaches when each sensing modality is used individually. The highest accuracy of 95.6% is obtained for the decision-level fusion approach.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.