ArticleSensors (Basel, Switzerland)2019
A Posture Recognition Method Based on Indoor Positioning Technology.
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 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- A deep learning AI model for determining the relationship between X-Ray detectors and patient positioning in chest radiography.PloS one · 2025Article
- A novel approach for joint indoor localization and activity recognition using a hybrid CNN-GRU and MRF framework.PloS one · 2025Article
- Detection of Low Back Physiotherapy Exercises With Inertial Sensors and Machine Learning: Algorithm Development and Validation.JMIR rehabilitation and assistive technologies · 2022Article
- Smart Monitoring and Control in the Future Internet of Things.Sensors (Basel, Switzerland) · 2021Article
- Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review.Sensors (Basel, Switzerland) · 2021Review
- Monitoring Movements of Ataxia Patient by Using UWB Technology.Sensors (Basel, Switzerland) · 2020Article
- An Integrated Method for Coding Trees, Measuring Tree Diameter, and Estimating Tree Positions.Sensors (Basel, Switzerland) · 2019Article
- Yoga Posture Recognition and Quantitative Evaluation with Wearable Sensors Based on Two-Stage Classifier and Prior Bayesian Network.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Posture recognition has been widely applied in fields such as physical training, environmental awareness, human-computer-interaction, surveillance system and elderly health care. The traditional methods consist of two main variations: machine vision methods and acceleration sensor methods. The former has the disadvantages of privacy invasion, high cost and complex implementation processes, while the latter has low recognition rate for still postures. A new body posture recognition scheme based on indoor positioning technology is presented in this paper. A single deployed indoor positioning system is constructed by installing wearable receiving tags at key points of the human body. The distance measurement method with ultra-wide band (UWB) radio is applied to position the key points of human body. Posture recognition is implemented by positioning. In the posture recognition algorithm, least square estimation (LSE) method and the improved extended Kalman filtering (iEKF) algorithm are respectively adopted to suppress the noise of the distances measurement and to improve the accuracy of positioning and recognition. The comparison of simulation results with the two methods shows that the improved extended Kalman filtering algorithm is more effective in error performance.
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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 OpenQuestion graph.