This study examined how knee sleeve application affects the knee adduction moment (KAM), knee adduction angle (KAA), and knee muscle activation during gait in eleven healthy individuals. Participants completed walking trials under four conditions: a control condition without a sleeve (Normal) and three sleeve conditions with distinct compression characteristics (Motion, Slim, and Strong). KAM, KAA, and surface electromyography (EMG) from five lower limb muscles were compared across conditions. Neither KAM nor KAA differed significantly among the conditions (p > 0.05). In contrast, knee extensor EMG was lower under specific sleeve conditions than under Normal (p < 0.05). Because muscle forces contribute substantially to knee contact force during gait, this reduced activation may indicate lower internal knee joint loading. These findings suggest that, in healthy individuals, certain knee sleeves may alter neuromuscular strategies without producing detectable changes in KAM or KAA, although further validation is needed.

Knee contact forces and knee stiffness are biomechanical factors worth considering for walking in knee osteoarthritis patients. However, it is challenging to acquire these factors in real time; thus, making it difficult to use them in robotic rehabilitation and assistive systems. This study investigated whether trained deep neural networks (DNNs) can capture the biomechanical factors only using kinematics during gait, which is possible to measure via sensors in real time. A public dataset of walking on the ground was analyzed through biomechanical analysis to train and test DNNs. Using the training dataset, several DNN topologies were explored via Bayesian optimization to tune the hyperparameters. After optimization, DNNs were trained to estimate the biomechanical factors in a supervised manner. The trained DNNs were then evaluated using two new datasets, which were not used in the training process. The trained DNNs estimated the biomechanical factors with a high level of accuracy in both types of test datasets. Results confirmed that DNNs can estimate the biomechanical factors based on only kinematics during gait.