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.
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Notes
- 1.
In this work, by StS we do in fact mean both ‘Sit-to-Stand’ and ‘Stand-to-Sit’, but will specify which of the two, if and when necessary.
- 2.
The frame-rate of the silhouette recorder varied according to different conditions and produced 10 fps on average.
- 3.
Although here we refer to the computation of the speed of ascent, the methodology applies identically for the speed of descent by simply using the negative sign in Eq. 1.
- 4.
The CG was estimated using the average of the Left, Right, Anterior and Posterior Superior Illiac skeletal joints.
- 5.
Available on GitHub: https://github.com/ale152/muvilab.
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Acknowledgements
This work was performed under the SPHERE IRC funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant EP/K031910/1. The authors wish to thank all the study subjects for their participation in this project and Rachael Gooberman-Hill, Andrew Judge, Ian Craddock, Ashley Blom, Michael Whitehouse and Sabrina Grant for their support with the HEmiSPHERE project. The HEmiSPHERE project was approved by the Research Ethics Committee (reference number: 17/SW/0121).
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Masullo, A., Burghardt, T., Perrett, T., Damen, D., Mirmehdi, M. (2019). Sit-to-Stand Analysis in the Wild Using Silhouettes for Longitudinal Health Monitoring. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11663. Springer, Cham. https://doi.org/10.1007/978-3-030-27272-2_15
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