Effects of different slopes on hip and ankle biomechanics of replaced and non-replaced limbs of patients with total knee arthroplasty during incline ramp walking.

Although knee biomechanics has been examined, hip and ankle biomechanics in incline ramp walking has not been explored for patients with total knee arthroplasty (TKA). The purpose of this study was to investigate the hip and ankle joint kinematic and kinetic biomechanics of different incline slopes for replaced limbs and non-replaced limbs in individuals with TKA compared to healthy controls. Twenty-five patients with TKR and ten healthy controls performed walking trials on four slope conditions of level (0°), 5°, 10° and 15° on a customized instrumented ramp system. A 3x4 (limb x slope) repeated analysis of variance was used to evaluate selected variables. The results showed a greater peak ankle dorsiflexion angle in the replaced limbs compared to healthy limbs. No significant interactions or limb main effect for other ankle and hip variables. The peak dorsiflexion angle, eversion angle and dorsiflexion moment were progressively higher in each comparison from level to 15°. The peak plantarflexion moment was also increased with each increase of slopes. Both the replaced and non-replaced limbs of patients with TKA had lower hip flexion moments than the healthy control limbs. Hip angle at contact and hip extension range of motion increased with each increase of slopes. Peak hip loading-response internal extension moment increased with each increase in slope and peak hip push-off internal flexion moment decreased with each increase of slope. Our results showed increased dorsiflexion in replaced limbs but no other compensations of hip and ankle joints of replaced limbs compared to non-replaced limbs and their healthy controls during incline walking, providing further support of using incline walking in rehabilitation for patients with TKA.

Validity, reliability, and bias between instrumented pedals and loadsol insoles during stationary cycling.

The purpose of this study was to evaluate the validity and reliability of the loadsol in measuring pedal reaction force (PRF) during stationary cycling as well as lower limb symmetry. Ten healthy participants performed bouts of cycling at 1kg, 2kg, and 3kg workloads (conditions) on a cycle ergometer. The ergometer was fitted with instrumented pedals and participants wore loadsol plantar pressure insoles. A 3 x 2 (Condition x Sensor Type) ANOVA was used to examine the differences in measured peak PRF, impulse, and symmetry indices. Root mean square error, intraclass correlation coefficients, and Passing-Bablok regressions were used to further assess reliability and validity. The loadsol demonstrated poor (< 0.5) to excellent (> 0.9) agreement as measured by intraclass correlation coefficients for impulse and peak PRF. Passing-Bablok regression revealed a systematic bias only when assessing all workloads together for impulse with no bias present when looking at individual workloads. The loadsol provides a consistent ability to measure PRF and symmetry when compared to a gold standard of instrumented pedals but exhibits an absolute underestimation of peak PRF. This study provides support that the loadsol can identify and track symmetry differences in stationary cycling which means there is possible usage for clinical scenarios and interventions in populations with bilateral asymmetries such as individuals with knee replacements, limb length discrepancies, diabetes, or neurological conditions. Further investigation of bias should be conducted in longer cycling sessions to ensure that the loadsol system is able to maintain accuracy during extended use.

Predicting Knee Joint Contact Forces During Normal Walking Using Kinematic Inputs With a Long-Short Term Neural Network.

Knee joint contact forces are commonly estimated via surrogate measures (i.e., external knee adduction moments or musculoskeletal modeling). Despite its capabilities, modeling is not optimal for clinicians or persons with limited experience. The purpose of this study was to design a novel prediction method for knee joint contact forces that is simplistic in terms of required inputs. This study included marker trajectories and instrumented knee forces during normal walking from the "Grand Challenge" (n = 6) and "CAMS" (n = 2) datasets. Inverse kinematics were used to derive stance phase hip (sagittal, frontal, transverse), knee (sagittal, frontal), ankle (sagittal), and trunk (frontal) kinematics. A long-short term memory network (LSTM) was created using matlab to predict medial and lateral knee force waveforms using combinations of the kinematics. The Grand Challenge and CAMS datasets trained and tested the network, respectively. Musculoskeletal modeling forces were derived using static optimization and joint reaction tools in OpenSim. Waveform accuracy was determined as the proportion of variance and root-mean-square error between network predictions and in vivo data. The LSTM network was highly accurate for medial forces (R2 = 0.77, RMSE = 0.27 BW) and required only frontal hip and knee and sagittal hip and ankle kinematics. Modeled medial force predictions were excellent (R2 = 0.77, RMSE = 0.33 BW). Lateral force predictions were poor for both methods (LSTM R2 = 0.18, RMSE = 0.08 BW; modeling R2 = 0.21, RMSE = 0.54 BW). The designed LSTM network outperformed most reports of musculoskeletal modeling, including those reached in this study, revealing knee joint forces can accurately be predicted by using only kinematic input variables.