Lower Limb Prosthesis

2009 ◽  
pp. 1-27
Author(s):  
Mark R. Pitkin
Keyword(s):  
Lower Limb ◽  
Lower Limb Prosthesis ◽  
Limb Prosthesis

Intent Recognition in a Powered Lower Limb Prosthesis Using Time History Information

2013 ◽  
Vol 42 (3) ◽  
pp. 631-641 ◽  
Author(s):  
Aaron J. Young ◽  
Ann M. Simon ◽  
Nicholas P. Fey ◽  
Levi J. Hargrove
Keyword(s):  
Lower Limb ◽  
Time History ◽  
Intent Recognition ◽  
History Information ◽  
Lower Limb Prosthesis ◽  
Limb Prosthesis ◽  
Powered Lower Limb Prosthesis

Nonlinear Passive Cam-Based Springs for Powered Ankle Prostheses

2015 ◽  
Vol 9 (1) ◽  
Author(s):  
Jonathan Realmuto ◽  
Glenn Klute ◽  
Santosh Devasia
Keyword(s):  
Lower Limb ◽  
Nonlinear Response ◽  
Experimental Results ◽  
Passive Element ◽  
Ankle Torque ◽  
Lower Limb Prosthesis ◽  
Limb Prosthesis ◽  
Elastic Elements

This article studies the design of passive elastic elements to reduce the actuator requirements for powered ankle prostheses. The challenge is to achieve most of the typically nonlinear ankle response with the passive element so that the active ankle-torque from the actuator can be small. The main contribution of this article is the design of a cam-based lower-limb prosthesis to achieve such a nonlinear ankle response. Results are presented to show that the addition of the cam-based passive element can reduce the peak actuator torque requirement substantially, by ∼74%. Moreover, experimental results are presented to demonstrate that the cam-based design can achieve a desired nonlinear response to within 10%.

Reduced cortical brain activity with the use of microprocessor-controlled prosthetic knees during walking

2018 ◽  
Vol 43 (3) ◽  
pp. 257-265 ◽  
Author(s):  
Saffran Möller ◽  
David Rusaw ◽  
Kerstin Hagberg ◽  
Nerrolyn Ramstrand
Keyword(s):  
Cognitive Processes ◽  
Lower Limb ◽  
Brain Activity ◽  
Control Group ◽  
Healthy Controls ◽  
Prosthetic Knee ◽  
Level Walking ◽  
Lower Limb Prosthesis ◽  
Prosthetic Knees ◽  
Limb Prosthesis

Background: Individuals using a lower-limb prosthesis indicate that they need to concentrate on every step they take. Despite self-reports of increased cognitive demand, there is limited understanding of the link between cognitive processes and walking when using a lower-limb prosthesis. Objective: The objective was to assess cortical brain activity during level walking in individuals using different prosthetic knee components and compare them to healthy controls. It was hypothesized that the least activity would be observed in the healthy control group, followed by individuals using a microprocessor-controlled prosthetic knee and finally individuals using a non-microprocessor-controlled prosthetic knee. Study design: Cross-sectional study. Methods: An optical brain imaging system was used to measure relative changes in concentration of oxygenated and de-oxygenated haemoglobin in the frontal and motor cortices during level walking. The number of steps and time to walk 10 m was also recorded. The 6-min walk test was assessed as a measure of functional capacity. Results: Individuals with a transfemoral or knee-disarticulation amputation, using non-microprocessor-controlled prosthetic knee ( n = 14) or microprocessor-controlled prosthetic knee ( n = 15) joints and healthy controls ( n = 16) participated in the study. A significant increase was observed in cortical brain activity of individuals walking with a non-microprocessor-controlled prosthetic knee when compared to healthy controls ( p < 0.05) and individuals walking with an microprocessor-controlled prosthetic knee joint ( p < 0.05). Conclusion: Individuals walking with a non-microprocessor-controlled prosthetic knee demonstrated an increase in cortical brain activity compared to healthy individuals. Use of a microprocessor-controlled prosthetic knee was associated with less cortical brain activity than use of a non-microprocessor-controlled prosthetic knee. Clinical relevance Increased understanding of cognitive processes underlying walking when using different types of prosthetic knees can help to optimize selection of prosthetic components and provide an opportunity to enhance functioning with a prosthesis.

scholarly journals Frequency and Circumstances of Falls Reported by Ambulatory Unilateral Lower Limb Prosthesis Users: A Secondary Analysis

PM&R ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 344-353 ◽  
Author(s):  
Janis Kim ◽  
Matthew J. Major ◽  
Brian Hafner ◽  
Andrew Sawers
Keyword(s):  
Lower Limb ◽  
Secondary Analysis ◽  
Lower Limb Prosthesis ◽  
Limb Prosthesis

Poster 1: Quality of Life and Satisfaction are Strongly Related to Mobility for Patients with a Lower Limb Prosthesis

PM&R ◽  
2018 ◽  
Vol 10 ◽  
pp. S1-S1
Author(s):  
Shane R. Wurdeman ◽  
Phillip M. Stevens ◽  
James H. Campbell
Keyword(s):  
Quality Of Life ◽  
Lower Limb ◽  
Lower Limb Prosthesis ◽  
Limb Prosthesis

scholarly journals Automatic Incremental Learning of Terrain Transitions in a Powered Below Knee Prosthesis

2020 ◽  
Author(s):  
Roman Stolyarov ◽  
Hugh Herr ◽  
Matt Carney
Keyword(s):  
Lower Limb ◽  
Incremental Learning ◽  
Prediction Accuracy ◽  
Knee Prosthesis ◽  
Estimation Algorithm ◽  
Limited Sample ◽  
Physiological Conditions ◽  
Lower Limb Prosthesis ◽  
Limb Prosthesis ◽  
High Range

Objective: This paper describes the developmentand preliminary offline validation of an algorithm facilitatingautomatic, self-contained learning of ground terrain transitionsin a lower limb prosthesis. This method allows for continuous,in-field convergence on an optimal terrain prediction accuracyfor a given walking condition, and is thus not limited bythe specific conditions and limited sample size of an in-labtraining scheme. Methods: We asked one subject with a below-kneeamputation to traverse level ground, stairs, and rampsusing a high-range-of-motion powered prosthesis while internalsensor data were remotely logged. We then used these datato develop a dynamic classification algorithm which predictsthe terrain of each stride and then continuously updates thepredictor using both data from the previous stride and anaccurate terrain back-estimation algorithm. Results: Across 100simulations randomizing stride order, our method attained amean next-stride prediction accuracy of ? 96%. This valuewas first reached after ? 200 strides, or about ? 5 minutesof walking. Conclusion and significance: These results demonstratea method for automatically learning the gait patternspreceding terrain transitions in a prosthesis without relyingon any external devices. By virtue of its dynamic learningscheme, application of this method in real-time would allow forcontinuous, in-field optimization of prediction accuracy across avariety of walking variables including physiological conditions,variable terrain geometries, control methodologies, and users.

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