Deep learning semantic segmentation for indoor terrain extraction: Toward better informing free-living wearable gait assessment

Jason Moore, Samuel Stuart, Richard Walker, Peter McMeekin, Fraser Young, Alan Godfrey

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)
44 Downloads (Pure)

Abstract

Contemporary approaches to gait assessment use wearable within free-living environments to capture habitual information, which is more informative compared to data capture in the lab. Wearables range from inertial to camera-based technologies but pragmatic challenges such as analysis of big data from heterogenous environments exist. For example, wearable camera data often requires manual time-consuming subjective contextualization, such as labelling of terrain type. There is a need for the application of automated approaches such as those suggested by artificial intelligence (AI) based methods. This pilot study investigates multiple segmentation models and proposes use of the PSPNet deep learning network to automate a binary indoor floor segmentation mask for use with wearable camera-based data (i.e., video frames). To inform the development of the AI method, a unique approach of mining heterogenous data from a video sharing platform (YouTube) was adopted to provide independent training data. The dataset contains 1973 image frames and accompanying segmentation masks. When trained on the dataset the proposed model achieved an Instance over Union score of 0.73 over 25 epochs in complex environments. The proposed method will inform future work within the field of habitual free-living gait assessment to provide automated contextual information when used in conjunction with wearable inertial derived gait characteristics. Clinical Relevance—Processes developed here will aid automated video-based free-living gait assessment.
Original languageEnglish
Title of host publication2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN)
Place of PublicationPiscataway. US
PublisherIEEE
Pages1-4
Number of pages4
ISBN (Electronic)9781665459259
ISBN (Print)9781665459266
DOIs
Publication statusPublished - 27 Sept 2022
Event2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, BSN 2022 - Ioannina, Greece
Duration: 27 Sept 202230 Sept 2022

Publication series

NameIEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN)
PublisherIEEE
ISSN (Print)2376-8886
ISSN (Electronic)2376-8894

Conference

Conference2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, BSN 2022
Country/TerritoryGreece
CityIoannina
Period27/09/2230/09/22

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