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Background/Objectives: Evaluations allowing patients to return to sport (RTS) after anterior cruciate ligament reconstruction (ACLR) should be multimodal, including a psychological evaluation. The goal of this study was to determine if there is a correlation between the ACL–return to sport after injury (ACL-RSI) score at 6 months post-ACLR and mid-term functional results. Methods: A total of 498 patients were assessed 6 months after primary ACLR using a composite test including isokinetics, hops, and ACL-RSI. A minimum of 3 years of follow-up was necessary. At the last follow-up, each patient completed clinical and functional evaluations, including the subjective International Knee Documentation Committee (IKDC) score, Tegner Activity Scale, Self Knee Value (SKV), and ACL-RSI score. The results were compared overall and item by item. Results: At the last follow-up, the mean SKV, Tegner, IKDC, and ACL-RSI scores were 86.8 ± 14.3%, 6 ± 2.1, 77 ± 11.9%, and 68.8 ± 25.7%, respectively. A significant correlation existed between the 6-month ACL-RSI score and each functional test (respectively, ρ = 0.189 p < 0.001; ρ = 0.174 p < 0.001; ρ = 0.237 p < 0.001). The ACL-RSI score was significantly higher than at 6 months after surgery (p < 0.001). Over half (59.2%) of the cohort returned to an equal or greater level of activity, and there was a significant correlation between the 6-month ACL-RSI score and post-surgery level of activity. Conclusions: Patients with better ACL-RSI scores at 6 months post-ACLR have better functional results in the medium term and are more likely to RTS. Our results show a correlation between psychological factors at 6 months, measured through the ACL-RSI score, and activity level at mid-term follow-up. This study underlines the relationship between RTS and psychological effects, and the importance of ACLR rehabilitation to focus on decreasing apprehension and fear. [ABSTRACT FROM AUTHOR]

The menisci increase the contact area of load bearing in the knee and thus disperse the mechanical stress via their circumferential tensile fibers. Traumatic meniscus injuries cause mechanical symptoms in the knee, and are more prevalent amongst younger, more active patients, compared to degenerative tears amongst the elderly population. Traumatic meniscus tears typically result from the load-and-shear mechanism in the knee joint. The treatment depends on the size, location, and pattern of the tear. For non-repairable tears, partial or total meniscal resection decreases its tensile stress and increases joint contact stress, thus potentiating the risk of arthritis. A longitudinal vertical tear pattern at the peripheral third red-red zone leads to higher healing potential after repair. The postoperative rehabilitation protocols after repair range from immediate weight-bearing with no range of motion restrictions to non-weight bearing and delayed mobilization for weeks. Pediatric and adolescent patients may require special considerations due to their activity levels, or distinct pathologies such as a discoid meniscus. Further biomechanical and biologic evidence is needed to guide surgical management, postoperative rehabilitation protocols, and future technology applications for traumatic meniscus injuries. [ABSTRACT FROM AUTHOR]

In this work, an IoT system with edge computing capability is proposed, facilitating the postoperative surveillance of patients who have undergone knee surgery. The main objective is to reliably identify whether a set of orthopedic rehabilitation exercises is executed correctly, which is critical since it is often necessary to supervise patients during the rehabilitation period so as to avoid injuries or long recovery periods. The proposed system leverages the Internet of Things (IoT) paradigm in combination with deep learning and edge computing to classify the extension–flexion movement of one's knee via embedded machine learning (ML) classification algorithms. The contribution of the proposed work is multilayered, as this paper proposes a system tackling the challenges at the embedded system level, algorithmic level, and user-friendliness level considering a performance evaluation, including the metrics at the power consumption level, delay level, and throughput requirement level, as well as its accuracy and reliability. Furthermore, as an outcome of this work, a dataset of labeled knee movements is freely available to the research community with no limitations. It also provides real-time movement detection with an accuracy reaching 100%, which is achieved with an ML model trained to fit a low-cost off-the-shelf Bluetooth Low Energy platform. The proposed edge computing approach allows predictions to be performed on device rather than solely relying on a Cloud service. This yields critical benefits in terms of wireless bandwidth and power conservation, drastically enhancing device autonomy while delivering reduced event detection latency. In particular, the "on device" implementation is able to yield a drastic 99.9% wireless data transfer reduction, a critical 39% prediction delay reduction, and a valuable 17% increase in the event prediction rate considering a reference period of 60 s. Finally, enhanced privacy comprises another significant benefit from the implemented edge computing ML model, as sensitive data can be processed on site and only events or predictions are shared with medical personnel. [ABSTRACT FROM AUTHOR]

Based on seminal research from the 1970s and 1980s, the myth that the knees should only move as far anterior during the barbell squat until they vertically align with the tips of the feet in the sagittal plane still exists today. However, the role of both the hip joint and the lumbar spine, which are exposed to high peak torques during this deliberate restriction in range of motion, has remained largely unnoticed in the traditional literature. More recent anthropometric and biomechanical studies have found disparate results regarding anterior knee displacement during barbell squatting. For a large number of athletes, it may be favorable or even necessary to allow a certain degree of anterior knee displacement in order to achieve optimal training outcomes and minimize the biomechanical stress imparted on the lumbar spine and hip. Overall, restricting this natural movement is likely not an effective strategy for healthy trained individuals. With the exception of knee rehabilitation patients, the contemporary literature suggests it should not be practiced on a general basis. [ABSTRACT FROM AUTHOR]

Dynamic loads have short and long-term effects in the rehabilitation of lower limb joints. However, an effective exercise program for lower limb rehabilitation has been debated for a long time. Cycling ergometers were instrumented and used as a tool to mechanically load the lower limbs and track the joint mechano-physiological response in rehabilitation programs. Current cycling ergometers apply symmetrical loading to the limbs, which may not reflect the actual load-bearing capacity of each limb, as in Parkinson's and Multiple Sclerosis diseases. Therefore, the present study aimed to develop a new cycling ergometer capable of applying asymmetric loads to the limbs and validate its function using human tests. The instrumented force sensor and crank position sensing system recorded the kinetics and kinematics of pedaling. This information was used to apply an asymmetric assistive torque only to the target leg using an electric motor. The performance of the proposed cycling ergometer was studied during a cycling task at three different intensities. It was shown that the proposed device reduced the pedaling force of the target leg by 19% to 40%, depending on the exercise intensity. This reduction in pedal force caused a significant reduction in the muscle activity of the target leg (p < 0.001), without affecting the muscle activity of the non-target leg. These results demonstrated that the proposed cycling ergometer device is capable of applying asymmetric loading to lower limbs, and thus has the potential to improve the outcome of exercise interventions in patients with asymmetric function in lower limbs. [ABSTRACT FROM AUTHOR]

This paper presents the design of an innovative device that applies dynamic mechanical load to human knee joints. Dynamic loading is employed by applying cyclic and periodic force on a target area. The repeated force loading was considered to be an effective modality for repair and rehabilitation of long bones that are subject to ailments like fractures, osteoporosis, osteoarthritis, etc. The proposed device design builds on the knowledge gained in previous animal and mechanical studies. It employs a modified slider-crank linkage mechanism actuated by a brushless Direct Current (DC) motor and provides uniform and cyclic force. The functionality of the device was simulated in a software environment and the structural integrity was analyzed using a finite element method for the prototype construction. The device is controlled by a microcontroller that is programmed to provide the desired loading force at a predetermined frequency and for a specific duration. The device was successfully tested in various experiments for its usability and full functionality. The results reveal that the device works according to the requirements of force magnitude and operational frequency. This device is considered ready to be used for a clinical study to examine whether controlled knee-loading could be an effective regimen for treating the stated bone-related ailments. [ABSTRACT FROM AUTHOR]