Estimating System State During Human Walking With a Powered Ankle-Foot Orthosis

Li, D.; Becker, Aaron T.; Shorter, K. Alex; Hsiao-Wecksler, Elizabeth T.

doi:10.1109/tmech.2011.2161769

Cited by 35 publications

(31 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Plantar pressure distribution contains rich information on human gait. However, most existing prosthesis-implemented and orthosis-implemented contact force measurement systems are only used for the detection of gait events or gait phases [26][27][28][29][30][31]. To our knowledge, no previous studies have investigated whether the information of contact force can be used to recognize multiple locomotion tasks (e.g.…”

Section: Introductionmentioning

confidence: 99%

A foot-wearable interface for locomotion mode recognition based on discrete contact force distribution

et al. 2015

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

A foot-wearable interface for locomotion mode recognition based on discrete contact force distribution

et al. 2015

View full text Add to dashboard Cite

“…2) Kinematic measurements of the joint angles provided by a video motion capture system. The details of the experimental setup appear in [8].…”

Section: A Description Of Experimentsmentioning

confidence: 99%

“…In our simulations, the measurements from the motion capture system are used to establish the ground truth -the true state θ t (see [8]). It is used to compare and evaluate the performance of the coupled oscillator filter.…”

Section: A Description Of Experimentsmentioning

confidence: 99%

“…Contribution to technology: The coupled oscillator particle filter is demonstrated by using experimental data taken from an Ankle-foot Orthosis (AFO) device. AFOs provide support for the lower leg and foot, and can assist with rehabilitation and restoration of mobility to an impaired limb function [8]. The performance of an AFO device depends crucially upon accurate estimation of the gait cycle by using on-board sensing and computing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Filtering with rhythms: Application to estimation of gait cycle

Tilton

Hsiao-Wecksler

Mehta

2012

2012 American Control Conference (ACC)

View full text Add to dashboard Cite

The aim of this paper is to describe a coupled oscillator model for Bayesian inference. The coupled oscillator model comprises of a large number of oscillators with meanfield coupling. The collective dynamics of the oscillators are used to solve an inference problem: the empirical distribution of the population encodes a 'belief state' (posterior distribution) that is continuously updated based on noisy measurements. In effect, the coupled oscillator model works as a particle filter.The framework is described here with the aid of a model problem involving estimation of a walking gait cycle. For this problem, the coupled oscillator particle filter is developed, and demonstrated on experimental data taken from an Ankle-foot Orthosis (AFO) device.

show abstract

“…This method is much simpler and more practical compared to 3-D motion measurement combined with the method for sensing ground reaction force. David [8] uses the data fusion method including ankle angle and the forces of both feet to establish a standard gait prediction model. The law of motion of the human gait is learned and trained by some machine learning algorithms such as nearest neighbour, time slicing, etc.…”

Section: Introductionmentioning

confidence: 99%

Method for Walking Gait Identification in a Lower Extremity Exoskeleton Based on C4.5 Decision Tree Algorithm

Guo

Jiang

2015

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

A gait identification method for a lower extremity exoskeleton is presented in order to identify the gait sub-phases in human-machine coordinated motion. First, a sensor layout for the exoskeleton is introduced. Taking the difference between human lower limb motion and human-machine coordinated motion into account, the walking gait is divided into five sub-phases, which are 'double standing', 'right leg swing and left leg stance', 'double stance with right leg front and left leg back', 'right leg stance and left leg swing', and 'double stance with left leg front and right leg back'. The sensors include shoe pressure sensors, knee encoders, and thigh and calf gyroscopes, and are used to measure the contact force of the foot, and the knee joint angle and its angular velocity. Then, five sub-phases of walking gait are identified by a C4.5 decision tree algorithm according to the data fusion of the sensors' information. Based on the simulation results for the gait division, identification accuracy can be guaranteed by the proposed algorithm. Through the exoskeleton control experiment, a division of five sub-phases for the human-machine coordinated walk is proposed. The experimental results verify this gait division and identification method. They can make hydraulic cylinders retract ahead of time and improve the maximal walking velocity when the exoskeleton follows the person's motion.

show abstract

Estimating System State During Human Walking With a Powered Ankle-Foot Orthosis

Cited by 35 publications

References 16 publications

A foot-wearable interface for locomotion mode recognition based on discrete contact force distribution

A foot-wearable interface for locomotion mode recognition based on discrete contact force distribution

Filtering with rhythms: Application to estimation of gait cycle

Method for Walking Gait Identification in a Lower Extremity Exoskeleton Based on C4.5 Decision Tree Algorithm

Contact Info

Product

Resources

About