More than 70% of all knee injuries in soccer occur in non-contact situations. It is known that increased lower limb dynamic knee valgus is associated with such situations. Little has been found out about differences in knee kinematics of the dominant (kicking) and non-dominant (supporting) leg during a single leg landing. A total of 114 male adolescent soccer players (age 14.6 ± 1.1 years) from elite (N = 66) and amateur soccer clubs (N = 48) performed a single leg drop landing down from a box. For each leg, the two-dimensional dynamic knee valgus angle (DKVA) was calculated. Paired t-tests were used to statistically determine significant differences between dominant and non-dominant leg DKVA, and t-tests were calculated between the two performance groups. Statistically significant differences (p < 0.05) were identified for the DKVA between the dominant and non-dominant leg for both amateur and elite players, showing a greater DKVA for the dominant leg. Group differences for the DKVA between amateur and elite players were not found, neither for the dominant, nor for the non-dominant leg. It can be concluded that the non-dominant leg showed more stable dynamics than the dominant leg during unilateral landing regardless of the player’s performance level. This could be due to adaptions to sport-specific requirements. Therefore, it is recommended that programs to prevent knee injuries among soccer players consider the dynamics of each leg individually.

Postural deficits such as hyperlordosis (hollow back) or hyperkyphosis (hunchback) are relevant health issues. Diagnoses depend on the experience of the examiner and are, therefore, often subjective and prone to errors. Machine learning (ML) methods in combination with explainable artificial intelligence (XAI) tools have proven useful for providing an objective, data-based orientation. However, only a few works have considered posture parameters, leaving the potential for more human-friendly XAI interpretations still untouched. Therefore, the present work proposes an objective, data-driven ML system for medical decision support that enables especially human-friendly interpretations using counterfactual explanations (CFs). The posture data for 1151 subjects were recorded by means of stereophotogrammetry. An expert-based classification of the subjects regarding the presence of hyperlordosis or hyperkyphosis was initially performed. Using a Gaussian progress classifier, the models were trained and interpreted using CFs. The label errors were flagged and re-evaluated using confident learning. Very good classification performances for both hyperlordosis and hyperkyphosis were found, whereby the re-evaluation and correction of the test labels led to a significant improvement (M_PRAUC = 0.97). A statistical evaluation showed that the CFs seemed to be plausible, in general. In the context of personalized medicine, the present study’s approach could be of importance for reducing diagnostic errors and thereby improving the individual adaptation of therapeutic measures. Likewise, it could be a basis for the development of apps for preventive posture assessment.

(1) Background: Mobile movement analysis systems, for example, those based on Inertial Measurement Units (IMUs), enable digital real-time methods of collecting data in workplace ergonomics, but the relationship between observational method scores such as Rapid Upper Limb Assessment (RULA), upper-body posture, and their influence on musculoskeletal discomfort, has not yet been well investigated. This field study aimed to evaluate the relationship of these variables in two different target groups: production and office workers. (2) Methods: There were 64 subjects (44 men and 20 women) participating. Data collection was divided into two categories: (1) Cornell Musculoskeletal Discomfort Questionnaire (CMDQ) (n = 64) and 3D stereophotogrammetric posture analysis (n = 58), and (2) Investigation of workload via IMU-based motion capture (MoCap) and the Borg CR-10 body map (n = 24). Correlation tests and regression analysis were performed using SPSS and MATLAB software to examine the relationship between the upper-body posture and RULA. Multivariate analysis of variance (MANOVA) was applied to examine group differences. (3) Results: The findings did not support the authors’ hypothesis that posture risk at work significantly correlates with static upper-body posture and musculoskeletal discomfort. Pelvic tilt had a weak but significant influence on RULA. The data revealed interesting trends in physical exertion, musculoskeletal discomfort, and differences between production and office workers. However, the statistical analysis did not support this. Such approaches have the potential to enhance the accuracy of assessment outcomes and, in turn, provide a stronger foundation for enhancing ergonomic conditions.

(1) Background: Occupational fatigue is a primary factor leading to work-related musculoskeletal disorders (WRMSDs). Kinematic and kinetic experimental studies have been able to identify indicators of WRMSD, but research addressing real-world workplace scenarios is lacking. Hence, the authors of this study aimed to assess the influence of physical strain on the Borg CR-10 body map, ergonomic risk scores, and foot pressure in a real-world setting. (2) Methods: Twenty-four participants (seventeen men and seven women) were included in this field study. Inertial measurement units (IMUs) (n = 24) and in-shoe plantar pressure measurements (n = 18) captured the workload of production and office workers at the beginning of their work shift and three hours later, working without any break. In addition to the two 12 min motion capture processes, a Borg CR-10 body map and fatigue visual analog scale (VAS) were applied twice. Kinematic and kinetic data were processed using MATLAB and SPSS software, resulting in scores representing the relative distribution of the Rapid Upper Limb Assessment (RULA) and Computer-Assisted Recording and Long-Term Analysis of Musculoskeletal Load (CUELA), and in-shoe plantar pressure. (3) Results: Significant differences were observed between the two measurement times of physical exertion and fatigue, but not for ergonomic risk scores. Contrary to the hypothesis of the authors, there were no significant differences between the in-shoe plantar pressures. Significant differences were observed between the dominant and non-dominant sides for all kinetic variables. (4) Conclusions: The posture scores of RULA and CUELA and in-shoe plantar pressure side differences were a valuable basis for adapting one-sided requirements in the work process of the workers. Traditional observational methods must be adapted more sensitively to detect kinematic deviations at work. The results of this field study enhance our knowledge about the use and benefits of sensors for ergonomic risk assessments and interventions.

This pilot study aimed to investigate the use of sensorimotor insoles in pain reduction, different orthopedic indications, and the wearing duration effects on the development of pain. Three hundred and forty patients were asked about their pain perception using a visual analog scale (VAS) in a pre–post analysis. Three main intervention durations were defined: VAS_post: up to 3 months, 3 to 6 months, and more than 6 months. The results show significant differences for the within-subject factor “time of measurement”, as well as for the between-subject factor indication (p < 0.001) and worn duration (p < 0.001). No interaction was found between indication and time of measurements (model A) or between worn duration and time of measurements (model B). The results of this pilot study must be cautiously and critically interpreted, but may support the hypothesis that sensorimotor insoles could be a helpful tool for subjective pain reduction. The missing control group and the lack of confounding variables such as methodological weaknesses, natural healing processes, and complementary therapies must be taken into account. Based on these experiences and findings, a RCT and systematic review will follow.

Background: Patellofemoral pain (PFP) significantly affects patients’ daily activities and consequently reduces their quality of life. Custom-made foot orthoses (FOs) are a common method of medical treatment that positively influences biomechanical factors such as the kinematics of the lower extremity and reduces pain perception in patients. However, there is a gap in research regarding the influence of different FO treatments on knee pain. Therefore, this study addresses the impact of biomechanical foot orthoses (BMFOs) and sensorimotor foot orthoses (SMFOs) on patients with foot deformity and PFP. Methods: A total of 26 participants (9 men, 17 women; 27.7 ± 10.7 years; 175.0 ± 0.1 cm; 75.7 ± 18.8 kg; BMI: 24.7 ± 5.6) took part in this randomized controlled clinical trial. In the pre-test, knee pain was evaluated using the Kujala Anterior Knee Pain Scale after the physician’s anamnesis and plantar pressure measurement. A 3-month intervention with SMFO and BMFO was performed, and weekly development was evaluated using 11-item visual analog scales (VASs). Repeated measures analyses of variance were used to assess differences between time of measurements (ToMs) and the interaction effect between ToMs and treatment groups (SMFO, BMFO). Results: Statistical analysis revealed no statistically significant interaction between ToMs and treatment groups but a significant main effect on Kujala anterior knee pain scores (M_Diff = 10.189; p = 0.014) and 12-week VAS (p = 0.001). Conclusions: The findings indicate that both treatment approaches effectively alleviated perceived knee pain in the PFP sample with foot deformity, with neither approach demonstrating superior efficacy. This trial was registered in the WHO International Clinical Trials Registry Platform (ICTRP) and German Clinical Trials Register (DRKS00035082).