Dynamic Walking Planning for Gait Rehabilitation Robot

2009 IEEE International Conference on Rehabilitation Robotics

Soh

et al. 2009

In this paper, we propose a gait generation concept for rehabilitation, in which GaitGen, a gait pattern generator is introduced. The concept of GaitGen will be explained. GaitGen aims to provide objective gait planning for rehabilitation. Gait-related studies have been carried out for the validation and enhancement of GaitGen. Motion capture system has been used for gait database collection. An established equation of normalized gait parameters has been studied via GAITRite. The paper ends with a summary of results, findings, and future works.

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Section: Gait Planning For Rehabilitationmentioning

confidence: 99%

Gait planning for effective rehabilitation - From gait study to application in clinical rehabilitation

2009 IEEE International Conference on Rehabilitation Robotics

Soh

et al. 2009

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“…The BWS apparatus restricted pelvic motion, causes the modification of pelvic motion planning. The restricted pelvic motion are planned according to the lower limb motions and desired foot trajectory [13]. Apparently, most of the motion planning is template based, whereby a motion template of subject with anatomy parameters similar to the targeted subject is required.…”

Section: Introductionmentioning

confidence: 99%

Natural gait parameters prediction for gait rehabilitation via artificial neural network

Luu

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

et al. 2010

Gait pattern planning is an important issue in robotic gait rehabilitation. Gait pattern is known to be related to gait parameters, such as cadence, stride length, and walking speed. Thus, prior before the discussion of gait pattern planning, the planning of gait parameters for natural walking should be addressed. This work utilizes multi-layer perceptron neural network (MLPNN) to predict natural gait parameters for a given subject. The inputs of the MLPNN are age, gender, body height, and body weight of the targeted subject. The MLPNN is trained to output a suitable walking speed and cadence for given subject. Two MLPNNs are trained to study the efficiency and accuracy in predicting the desired outputs, for two different setups. First setup is that the MLPNN is trained specifically for slow speed condition only. In second setup, the MLPNN is trained for both slow and normal speed conditions. The results of the MLPNNs are presented in this paper. The efficiency and accuracy of the MLPNNs are discussed.

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“…In another research work, the BWS apparatus restricts pelvic motion, and causes the modification of pelvic motion planning. The restricted pelvic motion are planned according to the lower limb motions and desired foot trajectory [7]. Apparently, most of the motion planning is template based, whereby a motion template of subject with anatomy parameters similar to the targeted subject is required.…”

Section: Introductionmentioning

confidence: 99%

Subject-specific gait parameters prediction for robotic gait rehabilitation via generalized regression neural network

Luu

2011 IEEE International Conference on Robotics and Biomimetics

et al. 2011