Evaluating the effect of equipping an unloading knee orthosis with local muscle vibrators on clinical parameters, muscular activation level, and medial contact force in patients with medial knee osteoarthritis: A randomized trial.

BACKGROUND: Unloading knee orthosis is prescribed for people with unicompartmental knee osteoarthritis (OA) to unload the damaged compartment. However, despite its benefits, wearing unloading knee orthoses in the long term may decrease knee muscle activity and have a side effect on knee OA progression rate. OBJECTIVES: Therefore, this study aimed to determine whether equipping an unloading knee orthosis with local muscle vibrators improves its effectiveness in improving clinical parameters, medial contact force (MCF), and muscular activation levels. METHODS: The authors performed a clinical evaluation on 14 participants (7 participants wearing vibratory unloading knee orthoses and 7 participants wearing conventional unloading knee orthoses) with medial knee OA. RESULTS: Wearing both orthoses (vibratory and conventional) for 6 weeks significantly improved ( p < 0.05) the MCF, pain, symptoms, function, and quality of life compared with the baseline assessment. Compared with the baseline assessment, the vastus lateralis muscle activation level significantly increased ( p = 0.043) in the vibratory unloading knee orthoses group. The vibratory unloading knee orthoses significantly improved the second peak MCF, vastus medialis activation level, pain, and function compared with conventional unloading knee orthoses ( p < 0.05). CONCLUSIONS: Given the potential role of medial compartment loading in the medial knee OA progression rate, both types of unloading knee orthoses (vibratory and conventional) have a potential role in the conservative management of medial knee OA. However, equipping the unloading knee orthoses with local muscle vibrators can improve its effectiveness for clinical and biomechanical parameters and prevent the side effects of its long-term use.

An artificial neural network for full-body posture prediction in dynamic lifting activities and effects of its prediction errors on model-estimated spinal loads.

Musculoskeletal models have indispensable applications in occupational risk assessment/management and clinical treatment/rehabilitation programs. To estimate muscle forces and joint loads, these models require body posture during the activity under consideration. Posture is usually measured via video-camera motion tracking approaches that are time-consuming, costly, and/or limited to laboratories. Alternatively, posture-prediction tools based on artificial intelligence can be trained using measured postures of several subjects performing many activities. We aimed to use our previous posture-prediction artificial neural network (ANN), developed based on many measured static postures, to predict posture during dynamic lifting activities. Moreover, effects of the ANN posture-prediction errors on dynamic spinal loads were investigated using subject-specific musculoskeletal models. Seven individuals each performed twenty-five lifting tasks while their full-body three-dimensional posture was measured by a 10-camera Vicon system and also predicted by the ANN as functions of the hand-load positions during the lifting activities. The measured and predicted postures (i.e., coordinates of 39 skin markers) and their model-estimated L5-S1 loads were compared. The overall root-mean-squared-error (RMSE) and normalized (by the range of measured values) RMSE (nRMSE) between the predicted and measured postures for all markers/tasks/subjects was equal to 7.4 cm and 4.1 %, respectively (R2 = 0.98 and p < 0.05). The model-estimated L5-S1 loads based on the predicted and measured postures were generally in close agreements as also confirmed by the Bland-Altman analyses; the nRMSE for all subjects/tasks was < 10 % (R2 > 0.7 and p > 0.05). In conclusion, the easy-to-use ANN can accurately predict posture in dynamic lifting activities and its predicted posture can drive musculoskeletal models.