Kaufman, Kenton R

Repeated durations of dynamic activity with high ground reaction forces (GRFs) and loading rates (LRs) can be beneficial to bone health. To fully characterize dynamic activity in relation to bone health, field-based measurements of gait kinetics are desirable to assess free-living lower-extremity loading. The study aims were to determine correlations of peak vertical GRF and peak vertical LR with ankle peak vertical accelerations, and of peak resultant GRF and peak resultant LR with ankle peak resultant accelerations, and to compare them to correlations with tibia, thigh, and waist accelerations. GRF data were collected as ten healthy subjects (26 [19–34] years) performed 8–10 walking trials at velocities ranging from 0.19 to 3.05 m/s while wearing ankle, tibia, thigh, and waist accelerometers. While peak vertical accelerations of all locations were positively correlated with peak vertical GRF and LR (r2 > .53, P < .001), ankle peak vertical accelerations were the most correlated (r2 > .75, P < .001). All peak resultant accelerations were positively correlated with peak resultant GRF and LR (r2 > .57, P < .001), with waist peak resultant acceleration being the most correlated (r2 > .70, P < .001). The results suggest that ankle or waist accelerometers give the most accurate peak GRF and LR estimates and could be useful tools in relating physical activity to bone health.

ABSTRACTFor total knee arthroplasty (TKA), neutral mechanical alignment produces balanced static knee loading. Dynamically, knee loading is affected by more than limb static alignment. We compared static and dynamic knee loading following TKA. Fifteen TKA patients were evaluated pre‐operatively and 2 months and 2 years post‐operatively. Tibiofemoral angles and medial tibial plateau loading were calculated. Pre‐operatively, static medial load was greater for varus than valgus knees. Post‐operatively, no relationship existed between tibiofemoral angle and static medial plateau load. Pre‐operatively and post‐operatively, dynamic medial load was not dependent on tibiofemoral angle. While all patients achieved equal static plateau load distributions at 2 years, only 47% had dynamic medial load distributions of 50 ± 10%. Static tibiofemoral alignment alone does not predict dynamic tibial loading. © 2014 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 32:???–???, 2014.

This study aimed to define accelerations measured at the waist and lower extremities over a range of gait velocities to provide reference data for choosing the appropriate accelerometer for field-based human activity monitoring studies. Accelerations were measured with a custom activity monitor (± 16g) at the waist, thighs, and ankles in 11 participants over a range of gait velocities from slow walking to running speeds. The cumulative frequencies and peak accelerations were determined. Cumulative acceleration amplitudes for the waist, thighs, and ankles during gait velocities up to 4.8 m/s were within the standard commercial g-range (± 6g) in 99.8%, 99.0%, and 96.5% of the data, respectively. Conversely, peak acceleration amplitudes exceeding the limits of many commercially available activity monitors were observed at the waist, thighs, and ankles, with the highest peaks at the ankles, as expected. At the thighs, and more so at the ankles, nearly 50% of the peak accelerations would not be detected when the gait velocity exceeds a walking velocity. Activity monitor choice is application specific, and investigators should be aware that when measuring high-intensity gait velocity activities with commercial units that impose a ceiling at ± 6g, peak accelerations may not be measured.

Gait analysis is useful for revealing pathologic tibial rotation in children with cerebral palsy (CP), thus guiding interventions of de-rotational osteotomy [1]. Surface marker misplacement may cause an inaccurate definition of the knee flexion axis, which generates downstream artifacts such as cross talk between knee rotation angles and internal rotation errors, misleading these patients’ assessments and surgical interventions [2,3]. Therefore, the purpose of this study was to develop and evaluate a novel method for defining the true knee flexion axis from existing marker trajectories during gait. We hypothesized that the devised method would accurately quantify the offset in the flexion axis due to a systematically misplaced marker and correct the downstream crosstalk and rotation errors of the knee joint angles associated with the marker misplacement.

Patient compliance is important when assessing movement, particularly in a free-living environment when patients are asked to don their own accelerometers. Reducing the number of accelerometers could increase patient compliance. The aims of this study were (1) to determine and compare the validity of different accelerometer combinations and placements for a previously developed posture and dynamic movement identification algorithm. Custom-built activity monitors, each containing one tri-axial accelerometer, were placed on the ankles, right thigh, and waist of 12 healthy adults. Subjects performed a protocol in the laboratory including static orientations of standing, sitting, and lying down, and dynamic movements of walking, jogging, transitions between postures, and fidgeting to simulate free-living activity. When only one accelerometer was used, the thigh was found to be the optimal placement to identify both movement and static postures, with a misclassification error of 10%, and demonstrated the greatest accuracy for walking/fidgeting and jogging classification with sensitivities and positive predictive value (PPVs) greater than 93%. When two accelerometers were used, the waist-thigh accelerometers identified movement and static postures with greater accuracy than the thigh-ankle accelerometers (with a misclassification error of 11% compared to 17%). However, the thigh-ankle accelerometers demonstrated the greatest accuracy for walking/ fidgeting and jogging classification with sensitivities and PPVs greater than 93%. Movement can be accurately classified in healthy adults using tri-axial accelerometers placed on one or two of the following sites: waist, thigh, or ankle. Posture and transitions require an accelerometer placed on the waist and an accelerometer placed on the thigh.

Recording body accelerations has been investigated previously to monitor health, metabolic energy expenditure, postural sway and falls [1]. Activity monitoring and the subsequent analysis of the accelerometry data have also proven useful in detecting physical activity levels among Parkinson’s [2] and osteoarthritis patients [3].

Intramuscular pressure (IMP) has been put forth as a surrogate measure for muscle force. As technological advancements have lead to the creation of smaller IMP microsensors, obtaining IMP readings in the clinic has come closer to becoming a minimally-invasive reality. However, appropriate use of data from these sensors relies upon an understanding of the mechanism of pressure changes within skeletal muscle. To that end, a constitutive model, representing muscle as a transversely isotropic, hyperelastic, and isovolumetric was created [1] for implementation in a finite element simulation. The purpose of this study was to validate this constitutive muscle model with passive elongation tests of skeletal muscle tissue from New Zealand White (NZW) rabbits. Reaction forces and hydrostatic pressures resulting from applied deformations were determined with the finite element modeling (FEM) approach and were compared with previously published experimental data [2].

Knee menisci are semi-lunar, fibrocartilaginous structures that convert applied compressive loads to circumferential hoop stresses which are attenuated at the tibial plateau via the meniscal attachments [1]. These specialized interfaces, located at the lateral anterior (LA), lateral posterior (LP), medial anterior (MA), and medial posterior (MP) horns, are crucial to maintaining mechanical functionality of menisci, thereby preventing osteoarthritis [2]. Soft-tissues subjected to compression and shear loads are known to possess proteoglycans, which aid in resisting these applied stresses by retaining interstitial fluid [1]. Interestingly, proteoglycans are also known to be present at the meniscus to bone interface [3]. Despite the presence of these biphasic attachments and their important role in joint load transfer, there have been no quantitative investigations of the mechanical environment within these attachments under physiological and pathological loads. Recent development of a novel pressure microsensor now allows direct observation of fluid pressure, which will aid in understanding the internal mechanical environment within the attachment sites. Previous work indicates that the attachment sites are geometrically, mechanically, and histologically different [4], thus it is hypothesized that the fluid pressures would likewise differ.

Gait analysis is an important tool in assessing the health and activity levels of patients and regular physical activity has been associated with health improvements in a number of populations. Step counting is one of the most commonly used measures of physical activity [1] and many studies have investigated the use of wearable sensors for step counts [2–4]. Their small size and light weight mean that they may be used in a free living environment and are suitable for home deployment. One of the main issues associated with step counts as a measure of physical activity is that a very high level of accuracy in step detection is needed.

Many single-phase, solid mechanics computational models of skeletal muscle have been created based on the assumptions that skeletal muscle is hyperelastic, transversely isotropic,1,2 and isovolumic. However, muscle has also been observed to have non-linear time-dependent behaviors that can be modeled using viscoelasticity or poroelasticity. The inherent viscoelasticity of muscle may not fully explain the time-dependent nature that may arise from extracellular fluid filtration3 and redistribution4 within the tissue. Further, intramuscular pressure (IMP) has been shown to have a heterogeneous distribution in muscle. This may be attributable to the movement of extracellular fluid, but the mechanisms of fluid redistribution are not completely understood. While a viscoelastic model may predict time-dependent responses of skeletal muscle, viscoelastic theory does not explain underlying causative tissue behaviors.5