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Clin Orthop Relat Res. 2011 Oct;469(10):2953-70. doi: 10.1007/s11999-011-1916-9. Epub 2011 May 20.

The 2011 ABJS Nicolas Andry Award: 'Lab'-in-a-knee: in vivo knee forces, kinematics, and contact analysis.

Author information

  • 1Shiley Center for Orthopaedic Research and Education at Scripps Clinic, 11025 North Torrey Pines Road, Suite 200, La Jolla, CA 92037, USA. ddlima@scripps.edu

Abstract

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 third-generation 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 (-12 to +4 mm), mediolateral translation (-1 to 1.5 mm), axial rotation (-3° to 12°), and adduction-abduction (-1° to +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.

PMID:
21598121
[PubMed - indexed for MEDLINE]
PMCID:
PMC3171531
Free PMC Article

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