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Contemporary total knee arthroplasty patients have increased expectations of returning to predisease function, including sexual activity (SA). The purpose of this study was to determine whether patients using a digital care management platform (DCMP) were more likely to have a higher rate and frequency of return to SA. We conducted an exploratory analysis of a prospective, multicenter, randomized controlled trial that enrolled patients undergoing total knee arthroplasty. A total of 304 patients were randomized to a DCMP (n = 119) providing preoperative and postoperative education regarding return to SA or standard postoperative care (control group; n = 185). Return to SA, assessed via questionnaire, patient-reported outcome measures, Timed Up and Go test, single leg stance, active range of motion and need for manipulation under anesthesia were assessed at 90 days postoperatively. More patients in the DCMP group returned to SA compared to control at 90 days (58.4 versus 39.6%, P =.018); however, the control group resumed SA sooner (33.1 versus 42.0 days, P =.023). Patients who returned to SA were younger (61.6 versus 65.9 year), more often men (56 versus 35%) (P <.001), higher performing on the Timed Up and Go and single leg stance tests (P <.001), and had greater active range of motion (P =.007). There were no differences in patient-reported outcome measures or need for manipulation under anesthesia between patients that returned to SA and those who did not. More patients using a DCMP resumed SA at 90 days; however, patients in the control group returned to SA sooner. Those who returned to SA were younger, possessed greater physical function, and were more often men. [ABSTRACT FROM AUTHOR] more...

The article examines the results of MPFL reconstruction surgery in rural and urban patients, revealing that urban patients generally had better functional outcomes, particularly in knee extensor strength tests. Disparities in rehabilitation outcomes between rural and urban populations were noted even 12 months post-surgery, suggesting that access to rehabilitation facilities may impact results. The document also covers various studies on patellar instability and its treatment, including gait pattern recovery, muscle strength assessment, predictive factors for surgery failure, and patient-reported outcomes. Additionally, the importance of addressing the needs of disabled individuals in public health support systems is discussed. [Extracted from the article] more...

• Paper presents a comprehensive robotic system for lower limb rehabilitation. • EEG, EMG, and knee bending angle are used for assessing knee pain level. • Attention-based CNN-TLSTM model is employed for knee pain level detection. • Proposed CNN-TLSTM model attains 95.88% accuracy. • An automated robotic knee rehabilitation device is developed. In the modern era, a significant percentage of people around the world suffer from knee pain-related problems. 'Knee pain' can be alleviated by performing knee rehabilitation exercises in the correct posture on a regular basis. In our research, an attention mechanism-based CNN-TLSTM (Convolution Neural Network-tanh Long Sort-Term Memory) network has been proposed for assessing the knee pain level of a person. Here, electroencephalogram (EEG) signals of the frontal, parietal, and temporal lobes, electromyography (EMG) signals of the hamstring and quadriceps muscles, and knee bending angle have been used for knee pain detection. First, the CNN network has been utilized for automated feature extraction from the EEG, knee bending angle, and EMG data, and subsequently, the TLSTM network has been used as a classifier. The trained CNN-TLSTM model can classify the knee pain level of a person into five categories, namely no pain, low pain, medium pain, moderate pain, and high pain, with an overall accuracy of 95.88 %. In the hardware part, a prototype of an automated robotic knee rehabilitation system has been designed to help a person perform three rehabilitation exercises, i.e., sitting knee bending, straight leg rise, and active knee bending, according to his/her pain level, without the presence of any physiotherapist. The novelty of our research lies in (i) designing a novel deep learning-based classifier model for broadly classifying knee pain into five categories, (ii) introducing attention mechanism into the TLSTM network to boost its classification performance, and (iii) developing a user-friendly rehabilitation device for knee rehabilitation. [ABSTRACT FROM AUTHOR] more...

The article states that the menisci provide several vital mechanical functions in the knee joint including as spacer between the femoral condyle and tibial plateau, as provider of shock absorption to the knee joint during walking, and as support in the overall lubrication of the articular surfaces. The tibiofemoral contact area decreases by approximately 50%, while the contact forces increase 2-fold to 3-fold following meniscectomy. A recommendation for the surgeon is offered. more...

The article discusses a study aimed at determining whether females with patellofemoral pain syndrome (PFPS) who perform hip strengthening before functional exercises show more improvements than females who perform quadriceps strengthening before the same functional exercises. Females with PFPS performed initial hip strengthening through either the hip or quadriceps. It concludes that both rehabilitation approaches improved function and lessened pain. more...

Patellofemoral pain (PFP) continues to be one of the most commonly diagnosed knee pathologies. Patellofemoral pain is a term used to describe any host of lesions in and around the anterior knee. Part 1 of this article described an evaluation and examination process to allow a more accurate diagnosis of this perplexing problem. Part 2 of this series describes the various classifications of PFP, followed by evidence-based treatment solutions for each category of lesion. [ABSTRACT FROM AUTHOR] more...

• A novel actigraphy encoding, segmentation and analysis system is proposed for processing vibroarthrography data to identify cartilage degeneration. • The proposed algorithm includes two unique contributions in physiological signal pre-processing and adaptive segmentation. • Two new actigraphy-specific features are introduced in this research which characterize limb movements using accelerometers. • The proposed system in this research work has the potential to be applied in a telehealth framework. Hectic lifestyle coupled sometimes with deficient choices in food and exercise, has led to an increase in the number of individuals being diagnosed with joint generation disorders. In addition, a substantial number of individuals in specialized occupations (such as army) or sports suffer from joint related injuries, thereby leading to surgical replacements and in some severe cases as disability. Standard methods of detecting joint disorders or disabilities include arthroscopy and arthrogram. Vibroarthrography (VAG) is a non-invasive technique wherein the specialist listens to sounds generated from joint movements recorded using actigraphs. In this study, we propose a system which encodes, analyses and segments actigraphy-based VAG data for assessing the severity of cartilage degeneration, and highlighting instances of activity which causes crackling sounds during limb movements. The proposed system encapsulates IoMT (Internet of Medical Things) requirements by providing efficient data compression and analysis at the source. Using an actigraphy dataset from 89 participants, our experiments yielded that the system is able to compress the actigraphy data into 3-bits per sample, thereby reducing the signal size by about 88%, without losing any vital limb movement information. This has been further validated using a simple pattern classification of 3-bit quantized VAG data from participants with healthy and unhealthy knee joints. The method, which yielded a recognition accuracy of 84.6%, as compared to raw VAG data which yielded 80% accurate results. In addition, the proposed adaptive segmentation scheme in the system, leverages the 3-bit encoding by correctly identifying 90% of the segments of interest from each VAG signal. [ABSTRACT FROM AUTHOR] more...