ArticleSensors (Basel, Switzerland)2022
Human Activity Recognition by Sequences of Skeleton Features.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- TExNet: long short-term fast fall detection based on attention enhancement and self-adaption.Medical & biological engineering & computing · 2026Article
- PIFR: A novel approach for analyzing pose angle-based human activity to automate fall detection in videos.PloS one · 2025Article
- Reduction of Vision-Based Models for Fall Detection.Sensors (Basel, Switzerland) · 2024Article
- Indoor Human Action Recognition Based on Dual Kinect V2 and Improved Ensemble Learning Method.Sensors (Basel, Switzerland) · 2023Article
- BERT for Activity Recognition Using Sequences of Skeleton Features and Data Augmentation with GAN.Sensors (Basel, Switzerland) · 2023Article
- Physiotherapy Exercise Classification with Single-Camera Pose Detection and Machine Learning.Sensors (Basel, Switzerland) · 2022Article
- Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction.Sensors (Basel, Switzerland) · 2022Article
- Fall Detection for Shipboard Seafarers Based on Optimized BlazePose and LSTM.Sensors (Basel, Switzerland) · 2022Article
- Automatic Detection of Horner Syndrome by Using Facial Images.Journal of healthcare engineering · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In recent years, much effort has been devoted to the development of applications capable of detecting different types of human activity. In this field, fall detection is particularly relevant, especially for the elderly. On the one hand, some applications use wearable sensors that are integrated into cell phones, necklaces or smart bracelets to detect sudden movements of the person wearing the device. The main drawback of these types of systems is that these devices must be placed on a person's body. This is a major drawback because they can be uncomfortable, in addition to the fact that these systems cannot be implemented in open spaces and with unfamiliar people. In contrast, other approaches perform activity recognition from video camera images, which have many advantages over the previous ones since the user is not required to wear the sensors. As a result, these applications can be implemented in open spaces and with unknown people. This paper presents a vision-based algorithm for activity recognition. The main contribution of this work is to use human skeleton pose estimation as a feature extraction method for activity detection in video camera images. The use of this method allows the detection of multiple people's activities in the same scene. The algorithm is also capable of classifying multi-frame activities, precisely for those that need more than one frame to be detected. The method is evaluated with the public UP-FALL dataset and compared to similar algorithms using the same dataset.
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What Socratic holds
Registered trials
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 Socratic graph.