Patient-Specific Pose Estimation in Clinical Environments

October 31, 2018

Kenny ChenPaolo GabrielAbdulwahab AlasfourChenghao GongWerner K. DoyleOrrin DevinskyDaniel FriedmanPatricia DuganLucia MelloniThomas ThesenDavid GondaShifteh SattarSonya WangVikash Gilja

Early Access Note:
Early Access articles are new content made available in advance of the final electronic or print versions and result from IEEE’s Preprint or Rapid Post processes. Preprint articles are peer-reviewed but not fully edited. Rapid Post articles are peer-reviewed and edited but not paginated. Both these types of Early Access articles are fully citable from the moment they appear in IEEE Xplore.

Abstract

Patient-Specific Pose Estimation in Clinical Environments

Reliable posture labels in hospital environments can augment research studies on neural correlates to natural behaviors and clinical applications that monitor patient activity. However, many existing pose estimation frameworks are not calibrated for these unpredictable settings. In this paper we propose a semi-automated approach for improving upper-body pose estimation in noisy clinical environments, whereby we adapt and build around an existing joint tracking framework to improve its robustness to environmental uncertainties. The proposed framework uses subject-specific convolutional neural network (CNN) models trained on a subset of a patient’s RGB video recording chosen to maximize the feature variance of each joint. Furthermore, by compensating for scene lighting changes and by refining the predicted joint trajectories through a Kalman filter with fitted noise parameters, the extended system yields more consistent and accurate posture annotations when compared to two state-of-the-art generalized pose tracking algorithms for three hospital patients recorded in two research clinics.

READ FULL ARTICLE ON IEEE XPLORE

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