scholarly journals Effect of joint contracture of lower limb on joint angle and joint moment during standing posture: A mechanics simulation study

2016 ◽  
Vol 65 (6) ◽  
pp. 511-520
Author(s):  
Takuma Inai ◽  
Mutsuaki Edama ◽  
Tomoya Takabayashi ◽  
Masayoshi Kubo
Keyword(s):  
Simulation Study ◽  
Lower Limb ◽  
Joint Angle ◽  
Joint Moment ◽  
Joint Contracture ◽  
Standing Posture

A standing posture support device that reduces laparoscopic surgeons’ occupational lower limb stress

2018 ◽  
Vol 28 (3) ◽  
pp. 151-156 ◽  
Author(s):  
Wataru Nishimoto ◽  
Hiroshi Kawahira ◽  
Yoshihiro Shimomura ◽  
Yuji Nishizawa ◽  
Masaaki Ito
Keyword(s):  
Lower Limb ◽  
Standing Posture ◽  
Support Device

scholarly journals Bicycling Phase Recognition for Lower Limb Amputees Using Support Vector Machine Optimized by Particle Swarm Optimization

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6533
Author(s):  
Xinxin Li ◽  
Zuojun Liu ◽  
Xinzhi Gao ◽  
Jie Zhang
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Knee Joint ◽  
Lower Limb ◽  
Joint Angle ◽  
Support Vector ◽  
Swarm Optimization ◽  
Knee Joint Angle ◽  
Phase Recognition ◽  
Lower Limb Amputees

A novel method for recognizing the phases in bicycling of lower limb amputees using support vector machine (SVM) optimized by particle swarm optimization (PSO) is proposed in this paper. The method is essential for enhanced prosthetic knee joint control for lower limb amputees in carrying out bicycling activity. Some wireless wearable accelerometers and a knee joint angle sensor are installed in the prosthesis to obtain data on the knee joint and ankle joint horizontal, vertical acceleration signal and knee joint angle. In order to overcome the problem of high noise content in the collected data, a soft-hard threshold filter was used to remove the noise caused by the vibration. The filtered information is then used to extract the multi-dimensional feature vector for the training of SVM for performing bicycling phase recognition. The SVM is optimized by PSO to enhance its classification accuracy. The recognition accuracy of the PSO-SVM classification model on testing data is 93%, which is much higher than those of BP, SVM and PSO-BP classification models.

Effects of smart phone use on lower limb joint angle and dynamic balance during gait

Work ◽  
2020 ◽  
Vol 65 (1) ◽  
pp. 31-37 ◽  
Author(s):  
Seon-Chil Kim ◽  
Woon-Su Cho ◽  
Sung-Hyoun Cho
Keyword(s):  
Lower Limb ◽  
Joint Angle ◽  
Dynamic Balance ◽  
Smart Phone

Ambulant adults with spastic cerebral palsy: The validity of lower limb joint angle measurements from sagittal video recordings

2012 ◽  
Vol 35 (2) ◽  
pp. 186-191 ◽  
Author(s):  
Kerstin L. Larsen ◽  
Grethe Maanum ◽  
Kathrine F. Frøslie ◽  
Reidun Jahnsen
Keyword(s):  
Cerebral Palsy ◽  
Lower Limb ◽  
Joint Angle ◽  
Spastic Cerebral Palsy ◽  
Video Recordings ◽  
Angle Measurements
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