Knee joint forces (KJF) are biomechanical measures used to infer the load on knee joint structures. The purpose of this study is to develop an artificial neural network (ANN) that estimates KJF during sport movements, based on data obtained by wearable sensors. Thirteen participants were equipped with two inertial measurement units (IMUs) located on the right leg. Participants performed a variety of movements, including linear motions, changes of direction, and jumps. Biomechanical modelling was carried out to determine KJF. An ANN was trained to model the association between the IMU signals and the KJF time series. The ANN-predicted KJF yielded correlation coefficients that ranged from 0.60 to 0.94 (vertical KJF), 0.64 to 0.90 (anterior–posterior KJF) and 0.25 to 0.60 (medial–lateral KJF). The vertical KJF for moderate running showed the highest correlation (0.94 ± 0.33). The summed vertical KJF and peak vertical KJF differed between calculated and predicted KJF across all movements by an average of 5.7% ± 5.9% and 17.0% ± 13.6%, respectively. The vertical and anterior–posterior KJF values showed good agreement between ANN-predicted outcomes and reference KJF across most movements. This study supports the use of wearable sensors in combination with ANN for estimating joint reactions in sports applications.

Joint moment measurements represent an objective biomechanical parameter of knee joint load in knee osteoarthritis (KOA). Wearable sensors in combination with machine learning techniques may provide solutions to develop assistive devices in KOA patients to improve disease treatment and to minimize risk of non-functional overreaching (e.g., pain). The purpose of this study was to develop an artificial neural network (ANN) that estimates external knee flexion moments (KFM) and external knee adduction moments (KAM) during various locomotion tasks, based on data obtained by two wearable sensors. Thirteen participants were instrumented with two inertial measurement units (IMUs) located on the right thigh and shank. Participants performed six different locomotion tasks consisting of linear motions and motions with a change of direction, while IMU signals as well as full body kinematics and ground reaction forces were synchronously recorded. KFM and KAM were determined using a full body biomechanical model. An ANN was trained to estimate the KFM and KAM time series using the IMU signals as input. Evaluation of the ANN was done using a leave-one-subject-out cross-validation. Concordance of the ANN-estimated KFM and reference data was categorized for five tasks (walking straight, 90° walking turn, moderate running, 90° running turn and 45° cutting maneuver) as strong (r ≥ 0.69, rRMSE ≤ 23.1) and as moderate for fast running (r = 0.65 ± 0.43, rRMSE = 25.5 ± 7.0%). For all locomotion tasks, KAM yielded a lower concordance in comparison to the KFM, ranging from weak (r ≤ 0.21, rRMSE ≥ 33.8%) in cutting and fast running to strong (r = 0.71 ± 0.26, rRMSE = 22.3 ± 8.3%) for walking straight. Smallest mean difference of classical discrete load metrics was seen for KFM impulse, 10.6 ± 47.0%. The results demonstrate the feasibility of using only two IMUs to estimate KFM and KAM to a limited extent. This methodological step facilitates further work that should aim to improve the estimation accuracy to provide valuable biofeedback systems for KOA patients. Greater accuracy of effective implementation could be achieved by a participant- or task-specific ANN modeling.

This is the first study examining functionality of subjects with anterior cruciate ligament (ACL) tears and a subsequent reconstruction comprehensively by multiple test sessions from pre- to six months post-reconstruction. The purpose was to evaluate if a generally applied rehabilitation program restores functionality to levels of healthy controls. Subjects with unilateral tears of the ACL were compared to matched healthy controls throughout the rehabilitation. 20 recreational athletes were tested: T₁ (preoperative), 6 weeks after tear; T₂, 6 weeks, T₃, 3 months, T₄, 6 months post-reconstruction. At all test sessions, subjects self-evaluated their activity level with the Tegner activity score and their knee state with the Knee Injury and Osteoarthritis Outcome Score. Passive range of motion during knee flexion and extension and leg circumference were measured as functional clinical tests. Bilateral countermovement jumps, one-leg jumps for distance and isometric force tests in knee flexion and extension with 90° and 110° knee angle were conducted as functional performance tests. For determination of functionality, leg symmetry indices (LSIs) were calculated by dividing values of the injured by the uninjured leg. In the ACL group most LSIs decreased from T₁ to T₂, and increased from T₂ and T₃ to T₄. LSIs of ACL subjects remained lower than LSIs of healthy controls at 6 months post-reconstruction in nearly all parameters. Self-evaluation of ACL subjects showed, additionally, that activity level was lower than the pre-injury level at 6 months post-reconstruction. Low LSIs and low self-evaluation indicate that knee joint functionality is not completely restored at 6 months post-reconstruction. The study shows that multiple comprehensive testing throughout the rehabilitation gives detailed images of the functional state. Therefore, the functional state of ACL reconstructed individuals should be evaluated comprehensively and continuously throughout the rehabilitation to detect persisting deficiencies detailed and adapt rehabilitation programs individually depending on the functionality.