A multiaxial force-sensing implantable tibial prosthesis

Kirking, Bryan; Krevolin, Janet; Townsend, Christopher P.; Colwell, Clifford W.; D’Lima, Darryl D.

doi:10.1016/j.jbiomech.2005.05.023

Cited by 89 publications

(71 citation statements)

References 17 publications

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“…One representative normal walking cycle was selected for analysis. For knee implant loads, three orthogonal forces and moments were measured at the center of the tibial post and level with the top surface of the tibial tray [25]. EMG data were collected from 13 muscles including the biceps femoris long head (BifemLH), semimembranosus (Semimem), medial gastrocnemius (MedGas), lateral gastrocnemius (LatGas), tensor fascia latae (TFL), vastus lateralis (VasLat), rectus femoris (RF), sartorius (Sart), gracilis (Grac), soleus (Sol), tibialis anterior (TA), peroneus longus (PerLong), and adductor magnus (AddMag).…”

Section: Methodsmentioning

confidence: 99%

Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking

Walter

Kinney

Banks

et al. 2014

Journal of Biomechanical Engineering

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The ability to predict patient-specific joint contact and muscle forces accurately could improve the treatment of walking-related disorders. Muscle synergy analysis, which decomposes a large number of muscle electromyographic (EMG) signals into a small number of synergy control signals, could reduce the dimensionality and thus redundancy of the muscle and contact force prediction process. This study investigated whether use of subject-specific synergy controls can improve optimization prediction of knee contact forces during walking. To generate the predictions, we performed mixed dynamic muscle force optimizations (i.e., inverse skeletal dynamics with forward muscle activation and contraction dynamics) using data collected from a subject implanted with a forcemeasuring knee replacement. Twelve optimization problems (three cases with four subcases each) that minimized the sum of squares of muscle excitations were formulated to investigate how synergy controls affect knee contact force predictions. The three cases were: (1) CalibrateþMatch where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously matched, (2) Precalibrateþ Predict where experimental knee contact forces were predicted using precalibrated muscle model parameters values from the first case, and (3) Calibrateþ Predict where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously predicted, all while matching inverse dynamic loads at the hip, knee, and ankle. The four subcases used either 44 independent controls or five synergy controls with and without EMG shape tracking. For the CalibrateþMatch case, all four subcases closely reproduced the measured medial and lateral knee contact forces (R 2 ! 0.94, root-mean-square (RMS) error < 66 N), indicating sufficient model fidelity for contact force prediction. For the PrecalibrateþPredict and CalibrateþPredict cases, synergy controls yielded better contact force predictions (0.61 < R 2 < 0.90, 83 N < RMS error < 161 N) than did independent controls (-0.15 < R 2 < 0.79, 124 N < RMS error < 343 N) for corresponding subcases. For independent controls, contact force predictions improved when precalibrated model parameter values or EMG shape tracking was used. For synergy controls, contact force predictions were relatively insensitive to how model parameter values were calibrated, while EMG shape tracking made lateral (but not medial) contact force predictions worse. For the subject and optimization cost function analyzed in this study, use of subject-specific synergy controls improved the accuracy of knee contact force predictions, especially for lateral contact force when EMG shape tracking was omitted, and reduced prediction sensitivity to uncertainties in muscle model parameter values.

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Section: Methodsmentioning

confidence: 99%

Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking

Walter

Kinney

Banks

et al. 2014

Journal of Biomechanical Engineering

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“…Ground reaction forces were recorded at 1000 Hz with three force plates (Type 2, Bertec Corp., Columbus OH, US). Knee forces were measured at 50 Hz using an instrumented tibial prosthesis capable of measuring all six components of the tibial load (Kirking et al 2006). The experiment was approved by the institutional review board, and subject had given informed consent to both data collection and public distribution.…”

Section: Experimental Datamentioning

confidence: 99%

Scaling of musculoskeletal models from static and dynamic trials

Lund

Andersen

Zee

et al. 2015

International Biomechanics

132

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Subject-specific scaling of cadaver-based musculoskeletal models is important for accurate musculoskeletal analysis within multiple areas such as ergonomics, orthopaedics and occupational health. We present two procedures to scale 'generic' musculoskeletal models to match segment lengths and joint parameters to a specific subject and compare the results to a simpler approach based on linear, segment-wise scaling. By incorporating data from functional and standing reference trials, the new scaling approaches reduce the model sensitivity to assumed model marker positions. For validation, we applied all three scaling methods to an inverse dynamics-based musculoskeletal model and compared predicted knee joint contact forces to those measured with an instrumented prosthesis during gait. Additionally, a Monte Carlo study was used to investigate the sensitivity of the knee joint contact force to random adjustments of the assumed model marker positions (+/− one marker diameter). The model based on linear scaling showed the highest variation in the knee joint contact force of 1.44 body weight (BW) around contra-lateral heel strike, and a variation in root mean square deviation (RMSD) of 0.36 BW. The proposed methods reduced the variation to 1.0 BW (RMSD 0.26 BW) for the anatomical landmark based method and 0.47 BW (RMSD 0.06 BW) for the functional based method. Variation in model predictions due to uncertainty in marker positions is a trait of all marker-based musculoskeletal modelling approaches. The presented methods solve part of this problem and rely less on manual identification of anatomical landmarks in the model. The work represents a step towards a more consistent methodology in musculoskeletal modelling.

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“…1), implanted in three patients, used 12 strain gauges that measured strain in the stem of the tibial tray, which were calibrated to yield the three forces and three moments acting on the tray. Details of the device specifications, accuracy, and preclinical testing have been reported [15,26]. The third-generation design involved miniaturization of the external power and data acquisition equipment to a wearable belt pack (*2 pounds), enabling continuous monitoring of knee forces and kinematics outside the laboratory.…”

Section: Implant Designmentioning

confidence: 99%

The 2011 ABJS Nicolas Andry Award: ‘Lab’-in-a-Knee: In Vivo Knee Forces, Kinematics, and Contact Analysis

D’Lima

Patil

Steklov

et al. 2011

Clinical Orthopaedics &Amp; Related Research

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Background Tibiofemoral forces are important in the design and clinical outcomes of TKA. We developed a tibial tray with force transducers and a telemetry system to directly measure tibiofemoral compressive forces in vivo. Knee forces and kinematics traditionally have been measured under laboratory conditions. Although this approach is useful for quantitative measurements and experimental studies, the extrapolation of results to clinical conditions may not always be valid. Questions/purposes We therefore developed wearable monitoring equipment and computer algorithms for classifying and identifying unsupervised activities outside the laboratory. Methods Tibial forces were measured for activities of daily living, athletic and recreational activities, and with orthotics and braces, during 4 years postoperatively. Additional measurements included video motion analysis, EMG, fluoroscopic kinematic analysis, and ground reaction force measurement. In vivo measurements were used to evaluate computer models of the knee. Finite element models were used for contact analysis and for computing knee kinematics from measured knee forces. A thirdgeneration system was developed for continuous monitoring of knee forces and kinematics outside the laboratory using a wearable data acquisition hardware. Results By using measured knee forces and knee flexion angle, we were able to compute femorotibial AP translation (À12 to +4 mm), mediolateral translation (À1 to 1.5 mm), axial rotation (À3°to 12°), and adduction-abduction (À1°t o +1°). The neural-network-based classification system was able to identify walking, stair-climbing, sit-to-stand, and stand-to-sit activities with 100% accuracy. Conclusions Our data may be used to improve existing in vitro models and wear simulators, and enhance prosthetic designs and biomaterials.

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A multiaxial force-sensing implantable tibial prosthesis

Cited by 89 publications

References 17 publications

Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking

Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking

Scaling of musculoskeletal models from static and dynamic trials

The 2011 ABJS Nicolas Andry Award: ‘Lab’-in-a-Knee: In Vivo Knee Forces, Kinematics, and Contact Analysis

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