Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/526233
Title: Human Movement Analysis as a Measure for Fatigue: A Hidden Markov-Based Approach
Authors: Electr. & Comput. Eng., Univ. of Waterloo, Waterloo, ON, Canada;Karg, Michelle ; Venture, G. ; Hoey, Jesse ; Kulic, Dana
subject: hidden Markov models; kinematics; patient monitoring; patient rehabilitation; sport; PHMM; fatigue measurement; human movement analysis; kinematics; optical motion capture; parametric hidden Markov model; patient monitoring; patient rehabilitation; single squat database; sport exercises; training exercise; Fatigue; Hidden Markov models; Joints; Kinematics; Linear regression; Muscles; Training; Fatigue; linear regression; parametric hidden Markov model;
Year: 2014
Publisher: IEEE
Abstract: Fatigue influences the way a training exercise is performed and alters the kinematics of the movement. Monitoring the increase of fatigue during rehabilitation and sport exercises is beneficial to avoid the risk of injuries. This study investigates the use of a parametric hidden Markov model (PHMM) to estimate fatigue from observing kinematic changes in the way the exercise is performed. The PHMM is compared to linear regression. A top-level hidden Markov model with variable state transitions incorporates knowledge about the progress of fatigue during the exercise and the initial condition of a subject. The approach is tested on a squat database recorded with optical motion capture. The estimates of fatigue for a single squat, a set of squats, and an entire exercise correlate highly with subjective ratings.
URI: http://localhost/handle/Hannan/239848
http://localhost/handle/Hannan/526233
ISSN: 1534-4320
volume: 22
issue: 3
Appears in Collections:2014

Files in This Item:
File SizeFormat 
6716986.pdf1.9 MBAdobe PDF
Title: Human Movement Analysis as a Measure for Fatigue: A Hidden Markov-Based Approach
Authors: Electr. & Comput. Eng., Univ. of Waterloo, Waterloo, ON, Canada;Karg, Michelle ; Venture, G. ; Hoey, Jesse ; Kulic, Dana
subject: hidden Markov models; kinematics; patient monitoring; patient rehabilitation; sport; PHMM; fatigue measurement; human movement analysis; kinematics; optical motion capture; parametric hidden Markov model; patient monitoring; patient rehabilitation; single squat database; sport exercises; training exercise; Fatigue; Hidden Markov models; Joints; Kinematics; Linear regression; Muscles; Training; Fatigue; linear regression; parametric hidden Markov model;
Year: 2014
Publisher: IEEE
Abstract: Fatigue influences the way a training exercise is performed and alters the kinematics of the movement. Monitoring the increase of fatigue during rehabilitation and sport exercises is beneficial to avoid the risk of injuries. This study investigates the use of a parametric hidden Markov model (PHMM) to estimate fatigue from observing kinematic changes in the way the exercise is performed. The PHMM is compared to linear regression. A top-level hidden Markov model with variable state transitions incorporates knowledge about the progress of fatigue during the exercise and the initial condition of a subject. The approach is tested on a squat database recorded with optical motion capture. The estimates of fatigue for a single squat, a set of squats, and an entire exercise correlate highly with subjective ratings.
URI: http://localhost/handle/Hannan/239848
http://localhost/handle/Hannan/526233
ISSN: 1534-4320
volume: 22
issue: 3
Appears in Collections:2014

Files in This Item:
File SizeFormat 
6716986.pdf1.9 MBAdobe PDF
Title: Human Movement Analysis as a Measure for Fatigue: A Hidden Markov-Based Approach
Authors: Electr. & Comput. Eng., Univ. of Waterloo, Waterloo, ON, Canada;Karg, Michelle ; Venture, G. ; Hoey, Jesse ; Kulic, Dana
subject: hidden Markov models; kinematics; patient monitoring; patient rehabilitation; sport; PHMM; fatigue measurement; human movement analysis; kinematics; optical motion capture; parametric hidden Markov model; patient monitoring; patient rehabilitation; single squat database; sport exercises; training exercise; Fatigue; Hidden Markov models; Joints; Kinematics; Linear regression; Muscles; Training; Fatigue; linear regression; parametric hidden Markov model;
Year: 2014
Publisher: IEEE
Abstract: Fatigue influences the way a training exercise is performed and alters the kinematics of the movement. Monitoring the increase of fatigue during rehabilitation and sport exercises is beneficial to avoid the risk of injuries. This study investigates the use of a parametric hidden Markov model (PHMM) to estimate fatigue from observing kinematic changes in the way the exercise is performed. The PHMM is compared to linear regression. A top-level hidden Markov model with variable state transitions incorporates knowledge about the progress of fatigue during the exercise and the initial condition of a subject. The approach is tested on a squat database recorded with optical motion capture. The estimates of fatigue for a single squat, a set of squats, and an entire exercise correlate highly with subjective ratings.
URI: http://localhost/handle/Hannan/239848
http://localhost/handle/Hannan/526233
ISSN: 1534-4320
volume: 22
issue: 3
Appears in Collections:2014

Files in This Item:
File SizeFormat 
6716986.pdf1.9 MBAdobe PDF