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Sit-to-Stand Analysis in the Wild using Silhouettes for Longitudinal Health Monitoring

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Sit-to-Stand Analysis in the Wild using Silhouettes for Longitudinal Health Monitoring. / Masullo, Alessandro; Burghardt, Tilo; Perrett, Toby; Aldamen, Dima; Mirmehdi, Majid.

2019. Paper presented at 16th International Conference on
Image Analysis and Recognition, .

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@conference{030af4bcf7cb4fd196170e59f496d6ac,
title = "Sit-to-Stand Analysis in the Wild using Silhouettes for Longitudinal Health Monitoring",
abstract = "We present the first fully automated Sit-to-Stand or Stand-to-Sit (StS) analysis framework for long-term monitoring of patients in free-living environments using video silhouettes. Our method adopts a coarse-to-fine time localisation approach, where a deep learning classifier identifies possible StS sequences from silhouettes, and a smart peak detection stage provides fine localisation based on 3D bounding boxes. We tested our method on data from real homes of participants and monitored patients undergoing total hip or knee replacement. Our results show 94.4{\%} overall accuracy in the coarse localisation and an error of 0.026 m/s in the speed of ascent measurement, highlighting important trends in the recuperation of patients who underwent surgery.",
author = "Alessandro Masullo and Tilo Burghardt and Toby Perrett and Dima Aldamen and Majid Mirmehdi",
year = "2019",
month = "8",
day = "3",
doi = "10.1007/978-3-030-27272-2_15",
language = "English",
note = "16th International Conference on<br/>Image Analysis and Recognition, ICIAR ; Conference date: 27-08-2019 Through 29-08-2019",
url = "http://www.aimiconf.org/iciar19/",

}

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TY - CONF

T1 - Sit-to-Stand Analysis in the Wild using Silhouettes for Longitudinal Health Monitoring

AU - Masullo, Alessandro

AU - Burghardt, Tilo

AU - Perrett, Toby

AU - Aldamen, Dima

AU - Mirmehdi, Majid

PY - 2019/8/3

Y1 - 2019/8/3

N2 - We present the first fully automated Sit-to-Stand or Stand-to-Sit (StS) analysis framework for long-term monitoring of patients in free-living environments using video silhouettes. Our method adopts a coarse-to-fine time localisation approach, where a deep learning classifier identifies possible StS sequences from silhouettes, and a smart peak detection stage provides fine localisation based on 3D bounding boxes. We tested our method on data from real homes of participants and monitored patients undergoing total hip or knee replacement. Our results show 94.4% overall accuracy in the coarse localisation and an error of 0.026 m/s in the speed of ascent measurement, highlighting important trends in the recuperation of patients who underwent surgery.

AB - We present the first fully automated Sit-to-Stand or Stand-to-Sit (StS) analysis framework for long-term monitoring of patients in free-living environments using video silhouettes. Our method adopts a coarse-to-fine time localisation approach, where a deep learning classifier identifies possible StS sequences from silhouettes, and a smart peak detection stage provides fine localisation based on 3D bounding boxes. We tested our method on data from real homes of participants and monitored patients undergoing total hip or knee replacement. Our results show 94.4% overall accuracy in the coarse localisation and an error of 0.026 m/s in the speed of ascent measurement, highlighting important trends in the recuperation of patients who underwent surgery.

U2 - 10.1007/978-3-030-27272-2_15

DO - 10.1007/978-3-030-27272-2_15

M3 - Paper

ER -