Objective In rodent osteoarthritis models, behavioral changes are often subtle and require highly sensitive methods to detect these changes. Gait analysis is one assay that may provide sensitive, quantitative measurement of these behavioral changes1. To increase detection sensitivity of gait assessments relative to spatiotemporal gait collection alone, we combined our spatiotemporal and dynamic gait collection systems. Using this combined system, gait was assessed in the rat medial meniscus transection model and monoiodoacetate injection model of knee osteoarthritis. Design 36 male Lewis rats were separated into medial meniscus transection (n=8), medial collateral ligament transection (n=8), skin incision (n=4), monoiodoacetate injection (n=8), and saline injection (n=8) groups. After initiation of osteoarthritis, gait data were collected weekly in each group out to 4 weeks. Results The medial meniscus transection and monoiodoacetate injection models produced unique pathologic gait profiles, with medial meniscus transection animals developing a shuffling gait and monoiodoacetate injection animals exhibiting antalgic gait. Spatiotemporal changes were also observed in the medial meniscus transection model at week 1 (p<0.01), but were not observed in the monoiodoacetate injection model until week 3 (p<0.01). Dynamic gait changes were observed in both models as early as 1 week post-surgery (p<0.01). Conclusion Combined analysis of spatiotemporal and dynamic gait data increased detection sensitivity for gait modification in two rat osteoarthritis models. Analyzing the combined gait data provided a robust characterization of the pathologic gait produced by each model. Furthermore, this characterization revealed different patterns of gait compensations in two common rat models of knee osteoarthritis.

Robot-assisted training is a promising tool under development for improving walking function based on repetitive goal-oriented task practice. The challenges in developing the controllers for gait training devices that promote desired changes in gait is complicated by the limited understanding of the human response to robotic input. A possible method of controller formulation can be based on the principle of bio-inspiration, where a robot is controlled to apply the change in joint moment applied by human subjects when they achieve a gait feature of interest. However, it is currently unclear how lower extremity joint moments are modulated by even basic gait spatio-temporal parameters. In this study, we investigated how sagittal plane joint moments are affected by a factorial modulation of two important gait parameters: gait speed and stride length. We present the findings obtained from 20 healthy control subjects walking at various treadmill-imposed speeds and instructed to modulate stride length utilizing real-time visual feedback. Implementing a continuum analysis of inverse-dynamics derived joint moment profiles, we extracted the effects of gait speed and stride length on joint moment throughout the gait cycle. Moreover, we utilized a torque pulse approximation analysis to determine the timing and amplitude of torque pulses that approximate the difference in joint moment profiles between stride length conditions, at all gait speed conditions. Our results show that gait speed has a significant effect on the moment profiles in all joints considered, while stride length has more localized effects, with the main effect observed on the knee moment during stance, and smaller effects observed for the hip joint moment during swing and ankle moment during the loading response. Moreover, our study demonstrated that trailing limb angle, a parameter of interest in programs targeting propulsion at push-off, was significantly correlated with stride length. As such, our study has generated assistance strategies based on pulses of torque suitable for implementation via a wearable exoskeleton with the objective of modulating stride length, and other correlated variables such as trailing limb angle.

Robot-assisted gait training is becoming increasingly common to support recovery of walking function after neurological injury. How to formulate controllers capable of promoting desired features in gait, i.e. goals, is complicated by the limited understanding of the human response to robotic input. A possible method to formulate controllers for goal-oriented gait training is based on the analysis of the joint torques applied by healthy subjects to modulate such goals. The objective of this work is to understand how sagittal plane joint torque is affected by two important gait parameters: gait speed (GS) and stride length (SL). We here present the results obtained from healthy subjects walking on a treadmill at different speeds, and asked to modulate stride length via visual feedback. Via principal component analysis, we extracted the global effects of the two factors on the peak-to-peak amplitude of joint torques. Next, we used a torque pulse approximation analysis to determine optimal timing and amplitude of torque pulses that approximate the SL-specific difference in joint torque profiles measured at different values of GS. Our results show a strong effect of GS on the torque profiles in all joints considered. In contrast, SL mostly affects the torque produced at the knee joint at early and late stance, with smaller effects on the hip and ankle joints. Our analysis generated a set of torque assistance profiles that will be experimentally tested using gait training robots.

1Robot-assisted training is a promising tool under development for improving walking 2 function based on repetitive goal-oriented task practice. The challenges in developing 3 the controllers for gait training devices that promote desired changes in gait is 4 complicated by the limited understanding of the human response to robotic input. A 5 possible method of controller formulation can be based on the principle of 6 bio-inspiration, where a robot is controlled to apply the change in joint moment applied 7 by human subjects when they achieve a gait feature of interest. However, it is currently 8 unclear how lower extremity joint moments are modulated by even basic gait 9 spatio-temporal parameters. 10In this study, we investigated how sagittal plane joint moments are affected by a 11 factorial modulation of two important gait parameters: gait speed and stride length. 12November 10, 2018 1/25We present the findings obtained from 20 healthy control subjects walking at various 13 treadmill-imposed speeds and instructed to modulate stride length utilizing real-time 14 visual feedback. Implementing a continuum analysis of inverse-dynamics derived joint 15 moment profiles, we extracted the effects of gait speed and stride length on joint 16 moment throughout the gait cycle. Moreover, we utilized a torque pulse approximation 17 analysis to determine the timing and amplitude of torque pulses that approximate the 18 difference in joint moment profiles between stride length conditions, at all gait speed 19 conditions. 20Our results show that gait speed has a significant effect on the moment profiles in all 21 joints considered, while stride length has more localized effects, with the main effect 22 observed on the knee moment during stance, and smaller effects observed for the hip 23 joint moment during swing and ankle moment during the loading response. Moreover, 24our study demonstrated that trailing limb angle, a parameter of interest in programs 25 targeting propulsion at push-off, was significantly correlated with stride length. As such, 26our study has generated assistance strategies based on pulses of torque suitable for 27 implementation via a wearable exoskeleton with the objective of modulating stride 28 length, and other correlated variables such as trailing limb angle. 29 Introduction 30 Robot-assisted training is a promising tool under development for improving walking 31 function [1, 2]. A primary indicator of gait performance improvement is gait speed (GS), 32 which is associated with a better quality of life [3] and overall functional status [4]. 33 Currently, it is not well understood how the modulation of assistance provided by a 34 robot during gait training will lead to changes in GS. The gait parameter of GS is 35 known to be correlated with anterior-posterior ground reaction force, the propulsive 36 force of the foot against the ground. Furthermore, propulsive impulse, the propulsive 37 force integrated over time, is associated with the posture of the trailing limb at 38 push-off [5]. The posture of ...

Conclusions:The results of this study support the hypothesis that side to side differences in mean full thickness UTE-T2* qMRI correlate with side to side differences in knee kinematics at 2 years after ACLR. The finding that a more lateral KCOR in the ACLR knee correlates with UTE T2* values in the medial tibia that were higher than the contralateral side suggests that this kinematic change, which has been previously shown to result in more relative motion between the femur and tibia in the medial compartment, could be affecting subsurface matrix integrity, inducing changes detectable by UTE-T2* mapping. Additionally, the finding that a more posterior KCOR in the ACLR knee correlated with UTE-T2* values in the lateral tibia that were lower than the contralateral knee further suggests that the UTE-T2* metric may reflect early changes in cartilage health. When interpreted within the context of prior work showing that a posterior shift in KCOR from 2 to 4 years post-surgery correlated with improved clinical outcomes at 8 years, the observed lower UTE-T2* with a more posterior KCOR, which is reflective of improved quadriceps recruitment, suggests positive cartilage matrix properties. In spite of the limitations of this cross-sectional and exploratory study, and the difficulty accounting for changes in the contralateral knee, these results support future studies of the relationship between UTE-T2* and KCOR to provide new insight into predicting the risk for OA after ACLR.