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 paper, 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 five 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 three 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 paper focused on the knee, the technology can be extended to examine the musculoskeletal tissue loads at different sites of the human body.

Tepping gait tasks. Methods A computational EMG-driven neuromusculoskeletal model was used to estimate the muscle and tibiofemoral contact forces in those with single-bundle combined semitendinosus and gracilis tendon autograft ACLR (n = 104, 29.7 ± 6.5 yr, 78.1 ± 14.4 kg) and healthy controls (n = 60, 27.5 ± 5.4 yr, 67.8 ± 14.0 kg) during walking (1.4 ± 0.2 m·s−1), running (4.5 ± 0.5 m·s−1) and sidestepping (3.7 ± 0.6 m·s−1). Within the computational model, the semitendinosus of ACLR participants was adjusted to account for literature reported strength deficits and morphological changes subsequent to autograft harvesting. Results ACLR had smaller maximum total and medial tibiofemoral contact forces (~80% of control values, scaled to bodyweight) during the different gait tasks. Compared with controls, ACLR were found to have a smaller maximum knee flexion moment, which explained the smaller tibiofemoral contact forces. Similarly, compared with controls, ACLR had both a smaller maximum knee flexion angle and knee flexion excursion during running and sidestepping, which may have concentrated the articular contact forces to smaller areas within the tibiofemoral joint. Mean relative muscle and external load contributions to the tibiofemoral contact forces were not significantly different between ACLR and controls. Conclusions ACLR had lower bodyweight-scaled tibiofemoral contact forces during walking, running, and sidestepping, likely due to lower knee flexion moments and straighter knee during the different gait tasks. The relative contributions of muscles and external loads to the contact forces were equivalent between groups....

Musculoskeletal tissues respond to optimal mechanical signals (e.g., strains) through anabolic adaptations, while mechanical signals above and below optimal levels cause tissue catabolism. If an individual’s physical behaviour could be altered to generate optimal mechanical signalling to musculoskeletal tissues, then targeted strengthening and/or repair would be possible. We propose new bioinspired technologies to provide real-time biofeedback of relevant mechanical signals to guide training and rehabilitation. In this review we provide a description of how wearable devices may be used in conjunction with computational rigid-body and continuum models of musculoskeletal tissues to produce real-time estimates of localised tissue stresses and strains. It is proposed that these bioinspired technologies will facilitate a new approach to physical training that promotes tissue strengthening and/or repair through optimal tissue loading.