Human Postural Sway Estimation from Noisy Observations

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

3 Citations (Scopus)


Postural sway is a reflection of brain signals that are generated to control a person’s balance. During the process of ageing, the postural sway changes, which increases the likelihood of a fall. Thus far, expensive specialist equipment is required, such as a force plate, in order to detect such changes over time, which makes the process costly and impractical. Our long-term goal is to investigate the use of inexpensive, everyday video technology as an alternative. This paper describes a study that establishes a 3-way correlation between the clinical gold standard (force plate), a highly accurate multi-camera 3D video tracking system (Vicon) and a standard RGB video camera. To this end, a dataset of 18 subjects performing the BESS balance test on the force plate was recorded, while simultaneously recording the 3D Vicon data, and the RGB video camera data. Then, using Gaussian process regression and a recurrent neural network, models were built to predict the lateral postural sway in the force plate data from the RGB video data. The predicted results show high correlation with the actual force plate signals, which supports the hypothesis that lateral postural sway can be accurately predicted from video data alone. Detecting changes to a person’s postural sway can be used to improve elderly people’s life by monitoring the likelihood of a fall and detecting its increase well before a fall occurs, so that countermeasures (e.g. exercises) can be put in place to prevent falls occurring.
Original languageEnglish
Title of host publicationProceedings of the 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017)
EditorsKevin Bowyer, Rama Chellappa, Jeff Cohn
Place of PublicationWashington
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781509040230
ISBN (Print)9781509040247
Publication statusPublished - 30 May 2017
Event12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) - Washington, DC, USA, Washington, DC, United States
Duration: 30 May 20173 Jun 2017

Publication series

ISSN (Print)2326-5396


Conference12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)
Country/TerritoryUnited States
CityWashington, DC


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