The use of inertial measurement units (IMUs) has gained popularity for the estimation of lower limb kinematics. However, implementations in clinical practice are still lacking. The aim of this review is twofold—to evaluate the methodological requirements for IMU-based joint kinematic estimation to be applicable in a clinical setting, and to suggest future research directions. Studies within the PubMed, Web Of Science and EMBASE databases were screened for eligibility, based on the following inclusion criteria: (1) studies must include a methodological description of how kinematic variables were obtained for the lower limb, (2) kinematic data must have been acquired by means of IMUs, (3) studies must have validated the implemented method against a golden standard reference system. Information on study characteristics, signal processing characteristics and study results was assessed and discussed. This review shows that methods for lower limb joint kinematics are inherently application dependent. Sensor restrictions are generally compensated with biomechanically inspired assumptions and prior information. Awareness of the possible adaptations in the IMU-based kinematic estimates by incorporating such prior information and assumptions is necessary, before drawing clinical decisions. Future research should focus on alternative validation methods, subject-specific IMU-based biomechanical joint models and disturbed movement patterns in real-world settings.

The ability to capture joint kinematics in outside-laboratory environments is clinically relevant. In order to estimate kinematics, inertial measurement units can be attached to body segments and their absolute orientations can be estimated. However, the heading part of such orientation estimates is known to drift over time, resulting in drifting joint kinematics. This study proposes a novel joint kinematic estimation method that tightly incorporates the connection between adjacent segments within a sensor fusion algorithm, to obtain drift-free joint kinematics. Drift in the joint kinematics is eliminated solely by utilizing common information in the accelerometer and gyroscope measurements of sensors placed on connecting segments. Both an optimization-based smoothing and a filtering approach were implemented. Validity was assessed on a robotic manipulator under varying measurement durations and movement excitations. Standard deviations of the estimated relative sensor orientations were below 0.89 • in an optimization-based smoothing implementation for all robot trials. The filtering implementation yielded similar results after convergence. The method is proven to be applicable in biomechanics, with a prolonged gait trial of 7 minutes on 11 healthy subjects. Three-dimensional knee joint angles were estimated, with mean RMS errors of 2.14 • , 1.85 • , 3.66 • in an optimization-based smoothing implementation and mean RMS errors of 3.08 • , 2.42 • , 4.47 • in a filtering implementation, with respect to a golden standard optical motion capture reference system. Tommy Verbeerst received the M.Sc. degree in electrical engineering from KHBO, Ostend, Belgium, in 2008. Since 2013, he has been working with KU Leuven and UC Vives. He is affiliated with the Department of Electrical Engineering (ESAT), KU Leuven Campus Bruges, Belgium. His current research interest includes the fields of engineering education, robotics, and machine-vision. Mark Versteyhe received the M.Sc. degree in mechanical engineering and the Ph.D. degree in applied sciences, from KU Leuven in 1995 and 2000, respetively.He has worked 16 years in industry in various functions linked to research and innovation. Since October 2016, he has been a Professor with KU Leuven's Faculty of Engineering Technology, Technology Campus Brugge, where he co-ordinates the research effort on connected mechatronics. His research focus lies in studying and applying the holistic paradigm of mechatronic system design. His special interest goes to "Dependability" which encompasses reliabilityavailability-robustness and security of a system and "Distributed Systems" which are treated as a complex ecosystem of machines and humans that are connected within the Industry 4.0 paradigm shift.Kurt Claeys received the M.Sc. degree in musculoskeletal rehabilitation sciences and physiotherapy from the University of Ghent, Belgium, in 1993, and the Ph.D. degree in orthopedic manual therapist from the IRSK-WINGS institute Ieper, Belgium, in 2005, and the Ph.D. degree from KU Leuven, Belgium,...

These findings suggest that inertial measurement units can be used for outside laboratory assessment (e.g. in a hospital environment) of temporal gait parameters in the knee arthroplasty population.

Background: Kinesiophobia is a psycho-cognitive factor that hampers recovery after orthopedic surgery. No evidence exists on the influence of kinesiophobia on the short-term recovery of function in patients with knee replacement (KR). Therefore, the aim of the present study is to investigate the impact of kinesiophobia on short-term patient-reported outcomes (PROMs) and performance-based measures (PBMs). Methods: Forty-three KR patients filled in the Tampa scale for kinesiophobia (TSK) at time of discharge. Patients with TSK ≥ 37 were allocated to the kinesiophobia group (n = 24), others to the no-kinesiophobia group (n = 19). Patients were asked to complete PROMs and to execute PBMs at discharge and at 6-weeks follow-up. An independent samples t-test was used to compare group differences for PROMs and PBMs at both measurement sessions. Multiple linear regression analysis models were used to model PBM outcomes from age, pain and TSK scores. Results: Significant differences were observed between groups for PROMs and PBMs. Kinesiophobia significantly contributed to the reduced functional outcomes. Conclusion: At discharge from the hospital, 55.8% of KR patients demonstrated high levels of kinesiophobia (TSK ≥ 37). This may negatively influence short-term recovery of these patients, by putting them at higher risk for falling and reduced functionality.

ObjectiveDesires and expectations of patients in regard to resume participation in sport activities after knee arthroplasty strongly increased in recent years. Therefore, this review systematically reviewed the available scientific literature on the effect of knee arthroplasty on sports participation and activity levels.DesignSystematic review and meta-analysis.Data sourcesPubMed, Embase, SPORTDiscus and reference lists were searched in February 2019.Studies eligibility criteriaInclusion of knee osteoarthritis patients who underwent total knee arthroplasty (TKA) and/or unicondylar knee arthroplasty. Studies had to include at least one preoperative and one postoperative measure (≥1 year post surgery) of an outcome variable of interest (ie, activity level: University of California, Los Angeles and/or Lower Extremity Activity Scale; sport participation: type of sport activity survey).ResultsNineteen studies were included, consisting data from 4074 patients. Knee arthroplasty has in general a positive effect on activity level and sport participation. Most patients who have stopped participating in sport activities in the year prior to surgery, however, do not seem to reinitiate their sport activities after surgery, in particular after a TKA. In contrast, patients who continue to participate in sport activities until surgery appear to become even more active in low-impact and medium-impact sports than before the onset of restricting symptoms.ConclusionsKnee arthroplasty is an effective treatment in resuming sports participation and physical activity levels. However, to achieve the full benefits from knee arthroplasty, strategies and guidelines aimed to keep patients capable and motivated to participate in (low-impact or medium-impact) sport activities until close before surgery are warranted.