Kinematics in the Terminal Swing Phase of Unilateral Transfemoral Amputees: Microprocessor-Controlled Versus Swing-Phase Control Prosthetic Knees

Maaref, K.; Martinet, N.; Grumillier, C.; Ghannouchi, Sonia Ayachi; André, J. M.; Paysant, Jean

doi:10.1016/j.apmr.2010.01.025

Cited by 38 publications

(34 citation statements)

References 20 publications

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“…Several studies have documented the improvement in gait and walking function when using micro-controlled prostheses compared with mechanically controlled devices (31,32,37,55). A more efficient and symmetrical gait (32,48,53,55), better stair descent ability (31), and better balance (31) have been observed in TFP users with micro-controlled devices compared with users with mechanically controlled devices.…”

Section: What Advances Have Been Made In Restoring Function For Tfp Umentioning

confidence: 98%

A biomechanical model for encoding joint dynamics: applications to transfemoral prosthesis control

McGibbon

2012

Journal of Applied Physiology

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McGibbon CA. A biomechanical model for encoding joint dynamics: applications to transfemoral prosthesis control. J Appl Physiol 112: 1600 -1611, 2012. First published January 26, 2012 doi:10.1152/japplphysiol.01251.2011.-This paper presents and tests a framework for encoding joint dynamics into energy states using kinematic and kinetic knee joint sensor data and demonstrates how to use this information to predict the future energy state (torque and velocity requirements) of the joint without a priori knowledge of the activity sequence. The intended application is for enhancing microcontrolled prosthetics by making use of the embedded sensory potential of artificial limbs and classical mechanical principles of a prosthetic joint to report instantaneous energy state and most probable next energy state. When applied to the knee during preferred and fast speed walking in 8 human subjects (66 preferred-speed trials and 50 fast-speed trials), it was found that joint energy states could be consistently sequenced (75% consensus) according to mechanical energy transference conditions and subsequences appeared to reflect the stability and energy dissipation requirements of the knee during gait. When simple constraints were applied to the energy transfer input conditions (their signs), simulations indicated that it was possible to predict the future energy state with an accuracy of Ͼ80% when 2% cycle in advance (ϳ20 ms) of the switch and Ͼ60% for 4% (ϳ40 ms) in advance. This study justifies future research to explore whether this encoding algorithm can be used to identify submodes of other human activity that are relevant to TFP control, such as chair and stair activities and their transitions from walking, as well as unexpected perturbations.

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Section: What Advances Have Been Made In Restoring Function For Tfp Umentioning

confidence: 98%

A biomechanical model for encoding joint dynamics: applications to transfemoral prosthesis control

McGibbon

2012

Journal of Applied Physiology

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“…In the interim period between the literature search and final submission of this review for publication, additional studies pertinent to the topic of this review were published [213][214][215][216]. Readers are encouraged to consider these publications in light of the findings presented here.…”

Section: Limitations Of Reviewmentioning

confidence: 99%

Outcomes associated with the use of microprocessor-controlled prosthetic knees among individuals with unilateral transfemoral limb loss: A systematic review

Sawers¹,

Hafner²

2013

JRRD

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Abstract-Microprocessor-controlled prosthetic knees (MPKs) have been developed as an alternative to non-microprocessorcontrolled knees (NMPKs) to address challenges facing individuals with lower-limb loss. A body of scientific literature comparing MPKs and NMPKs exists but has yet to be critically appraised. Therefore, we conducted a systematic review to examine outcomes associated with the use of these interventions among individuals with transfemoral limb loss. A search of biomedical databases identified 241 publications, of which 27 met the inclusion and exclusion criteria and were reviewed for methodological quality and content. We developed 28 empirical evidence statements (EESs) in 9 outcome categories (metabolic energy expenditure, activity, cognitive demand, gait mechanics, environmental obstacle negotiation, safety, preference and satisfaction, economics, and health and quality of life) based on findings in the literature. The level of evidence supporting these EESs varied due to quantity, quality, and consistency of the results. EESs supported by a moderate level of evidence that noted significant differences between MPKs and NMPKs were derived in five of the nine outcome categories. The results from this review suggest that evidence exists to inform clinical practice and that additional research is needed to confirm existing evidence and better understand outcomes associated with the use of NMPKs and MPKs.Key words: amputation, artificial limb, function, gait, knee, microprocessor, outcome, preference, rehabilitation, safety. Abbreviations: AAOP = American Academy of Orthotists andProsthetists, ABC = Activities-specific Balance Confidence, CWS = controlled walking speed, DLW = doubly labeled water, EBP = evidence-based practice, EES = empirical evidence statement, EV = external validity, GRF = ground reaction force, HAI = Hill Assessment Index, HRQL = health-related quality of life, IV = internal validity, LLL = lower-limb loss, MFCL = Medicare Functional Classification Level, MP = microprocessor, MPC = microprocessor control, MPK = microprocessor-controlled prosthetic knee, MRPP = Montreal Rehabilitation Performance Profile, NMPK = non-microprocessor-controlled prosthetic knee, O 2 = oxygen, O&P = orthotic and prosthetic, PAEE = physical activity-related energy expenditure, PCBS = Prosthetic Cognitive Burden Scale, PCI = physiological cost index, PEQ = Prosthesis Evaluation Questionnaire, PEQ-A = PEQ Addendum, PRO = patient-reported outcome, QALY = quality adjusted life year, QOL = quality of life, RPE = rating of perceived exertion, SAI = Stair Assessment Index, SF-36 = 36-Item Short-Form Health Survey, SOT = sensory organization test, SSWS = selfselected walking speed, SSC = State of the Science Conference, TDEE = total daily energy expenditure, TFLL = transfemoral limb loss.

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“…Most modern prosthetic knee joints consist of at least four bar linkage knee joints or are computer controlled. These types of knee joints influence the trajectory of the foot [28,29,34]. Nevertheless, to gain insight into the prosthetics dynamics, the single axis knee joint satisfies our requirements.…”

Section: Discussionmentioning

confidence: 97%

Principles of obstacle avoidance with a transfemoral prosthetic limb

Keeken

Vrieling

Hof

et al. 2012

Medical Engineering & Physics

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Kinematics in the Terminal Swing Phase of Unilateral Transfemoral Amputees: Microprocessor-Controlled Versus Swing-Phase Control Prosthetic Knees

Cited by 38 publications

References 20 publications

A biomechanical model for encoding joint dynamics: applications to transfemoral prosthesis control

A biomechanical model for encoding joint dynamics: applications to transfemoral prosthesis control

Outcomes associated with the use of microprocessor-controlled prosthetic knees among individuals with unilateral transfemoral limb loss: A systematic review

Principles of obstacle avoidance with a transfemoral prosthetic limb

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