Výsledky vyhledávání

Report

Estimating Lower Body Kinematics using a Lie Group Constrained Extended Kalman Filter and Reduced IMU Count

Autor: Sy, Luke Wicent, Lovell, Nigel H., Redmond, Stephen J.

Goal: This paper presents an algorithm for estimating pelvis, thigh, shank, and foot kinematics during walking using only two or three wearable inertial sensors. Methods: The algorithm makes novel use of a Lie-group-based extended Kalman filter. The

Externí odkaz: http://arxiv.org/abs/2103.11393

Zobrazit plný text záznamu

Report

Estimating Lower Limb Kinematics using Distance Measurements with a Reduced Wearable Inertial Sensor Count

Autor: Sy, Luke, Lovell, Nigel H., Redmond, Stephen J.

This paper presents an algorithm that makes novel use of distance measurements alongside a constrained Kalman filter to accurately estimate pelvis, thigh, and shank kinematics for both legs during walking and other body movements using only three wea

Externí odkaz: http://arxiv.org/abs/2003.10228

Zobrazit plný text záznamu

Report

Estimating Lower Limb Kinematics using a Lie Group Constrained EKF and a Reduced Wearable IMU Count

Autor: Sy, Luke, Lovell, Nigel H., Redmond, Stephen J.

This paper presents an algorithm that makes novel use of a Lie group representation of position and orientation alongside a constrained extended Kalman filter (CEKF) to accurately estimate pelvis, thigh, and shank kinematics during walking using only

Externí odkaz: http://arxiv.org/abs/1910.01808

Zobrazit plný text záznamu

Report

Estimating Lower Limb Kinematics using a Reduced Wearable Sensor Count

Autor: Sy, Luke, Raitor, Michael, Del Rosario, Michael, Khamis, Heba, Kark, Lauren, Lovell, Nigel H., Redmond, Stephen J.

Goal: This paper presents an algorithm for accurately estimating pelvis, thigh, and shank kinematics during walking using only three wearable inertial sensors. Methods: The algorithm makes novel use of a constrained Kalman filter (CKF). The algorithm

Externí odkaz: http://arxiv.org/abs/1910.00910

Zobrazit plný text záznamu

Report

An Empirical Evaluation of Deep Learning for ICD-9 Code Assignment using MIMIC-III Clinical Notes

Autor: Huang, Jinmiao, Osorio, Cesar, Sy, Luke Wicent

Background and Objective: Code assignment is of paramount importance in many levels in modern hospitals, from ensuring accurate billing process to creating a valid record of patient care history. However, the coding process is tedious and subjective,

Externí odkaz: http://arxiv.org/abs/1802.02311

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání