ArticleComputational intelligence and neuroscience2016
A Human Activity Recognition System Using Skeleton Data from RGBD Sensors.
Article in Computational intelligence and neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Machine Learning for Human Activity Recognition: State-of-the-Art Techniques and Emerging Trends.Journal of imaging · 2025Review
- Implications of Aging in Place in the Context of the Residential Environment: Bibliometric Analysis and Literature Review.International journal of environmental research and public health · 2023Review
- Recurrent Network Solutions for Human Posture Recognition Based on Kinect Skeletal Data.Sensors (Basel, Switzerland) · 2023Article
- Multi-View Human Action Recognition Using Skeleton Based-FineKNN with Extraneous Frame Scrapping Technique.Sensors (Basel, Switzerland) · 2023Article
- Ambient assisted living for frail people through human activity recognition: state-of-the-art, challenges and future directions.Frontiers in neuroscience · 2023Review
- Pixels2Pose: Super-resolution time-of-flight imaging for 3D pose estimation.Science advances · 2022Article
- Markerless 3D Skeleton Tracking Algorithm by Merging Multiple Inaccurate Skeleton Data from Multiple RGB-D Sensors.Sensors (Basel, Switzerland) · 2022Article
- Human activity recognition in artificial intelligence framework: a narrative review.Artificial intelligence review · 2022Article
- Student Behavior Recognition System for the Classroom Environment Based on Skeleton Pose Estimation and Person Detection.Sensors (Basel, Switzerland) · 2021Article
- Prediction of Human Activities Based on a New Structure of Skeleton Features and Deep Learning Model.Sensors (Basel, Switzerland) · 2020Article
- Sleep Apnea Detection with Polysomnography and Depth Sensors.Sensors (Basel, Switzerland) · 2020Article
- Deep artificial neural network based on environmental sound data for the generation of a children activity classification model.PeerJ. Computer science · 2020Article
- Spatio⁻Temporal Image Representation of 3D Skeletal Movements for View-Invariant Action Recognition with Deep Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2019Article
- Compositional Learning of Human Activities With a Self-Organizing Neural Architecture.Frontiers in robotics and AI · 2019Article
- Multiview Layer Fusion Model for Action Recognition Using RGBD Images.Computational intelligence and neuroscience · 2018Article
- Heart Rate Detection Using Microsoft Kinect: Validation and Comparison to Wearable Devices.Sensors (Basel, Switzerland) · 2017Article
- A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data.Sensors (Basel, Switzerland) · 2017Article
- Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis.Sensors (Basel, Switzerland) · 2016Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed.
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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.