Biofeedback for Gait Retraining Based on Real-Time Estimation of Tibiofemoral Joint Contact Forces

Abstract

Biofeedback assisted rehabilitation and intervention technologies have the potential to modify clinically relevant biomechanics. Gait retraining has been used to reduce the knee adduction moment, a surrogate of medial tibiofemoral joint loading often used in knee osteoarthritis research. In this study we present an electromyogram-driven neuromusculoskeletal model of the lower-limb to estimate, in real-time, the tibiofemoral joint loads. The model included 34 musculotendon units spanning the hip, knee, and ankle joints. Full-body inverse kinematics, inverse dynamics, and musculotendon kinematics were solved in real-time from motion capture and force plate data to estimate the knee medial tibiofemoral contact force (MTFF). We analyzed 5 healthy subjects while they were walking on an instrumented treadmill with visual biofeedback of their MTFF. Each subject was asked to modify their gait in order to vary the magnitude of their MTFF. All subjects were able to increase their MTFF, whereas only 3 subjects could decrease it, and only after receiving verbal suggestions about possible gait modification strategies. Results indicate the important role of knee muscle activation patterns in modulating the MTFF. While this study focused on the knee, the technology can be extended to examine the musculoskeletal tissue loads at different sites of the human body.

Gait Mechanics in Women of the ACL‐SPORTS Randomized Control Trial: Interlimb Symmetry Improves Over Time Regardless of Treatment Group

“…Participants walked with generally symmetric gait patterns by 2 years after ACLR, but not at earlier timepoints. Our findings suggest that meaningful movement asymmetries persist during gait even after participants achieve symmetrical strength, have high functional performance, and return to sports, supporting the notion that more time is needed to recover after ACLR and/or task‐specific training is needed to restore movement symmetry …”

“…Movement asymmetries were not resolved globally until 2 years after ACLR, long after most participants had achieved symmetrical strength and functional performance and returned to sports . These findings indicate that time and the physical challenge accompanying return to sports may be important considerations for restoring movement asymmetry, although targeted interventions may expedite this process . Future research studies examining real‐time visual, auditory, and/or tactile feedback to correct aberrant movement patterns are warranted.…”

Deep Learning for Musculoskeletal Force Prediction

“…Real time feedback is not new5, 27 but until now true real time rates have often required a number of compromises in terms of the complexity of the employed modelling. In contrast, the approach used here, by bypassing the computationally demanding explicit execution of inverse kinematics, inverse dynamics and static optimisation, promises the ability to build networks based on the results of models of increasing complexity, including those that so far have failed to achieve widespread use in large part because of their intractability to computation, such as those based on dynamic optimisation3.…”

“…More recently, EMG-driven models have been run close to real time. One such system was used to provide feedback on kinetic variables to subjects walking on a treadmill, but with a latency of 115ms27, far in excess of the maximum 75ms considered optimal for real time biofeedback(15). …”

Finding the sweet spot via personalised Achilles tendon training: the future is within reach

“…It will also be important to ensure the complex interplay between lower-limb kinematics, kinetics and muscle forces is accurately represented within the modelling framework. Sophisticated neuromusculoskeletal models that predict musculoskeletal tissue loading in real time from measured electromyograms,14 as well as wearable sensors to directly measure tendon force,15 have been developed and are currently being coupled to surrogate continuum models of the Achilles tendon to estimate localised tendon strain in real time. The aforementioned imaging methods, wearable sensors and computational models currently exist and have been validated independently, but require seamless integration before they can be applied in training and rehabilitation settings.…”