Hallux valgus, a frequently seen foot deformity, requires early detection to prevent it from becoming more severe. It is a medical economic problem, so a means of quickly distinguishing it would be helpful. We designed and investigated the accuracy of an early version of a tool for screening hallux valgus using machine learning. The tool would ascertain whether patients had hallux valgus by analyzing pictures of their feet. In this study, 507 images of feet were used for machine learning. Image preprocessing was conducted using the comparatively simple pattern A (rescaling, angle adjustment, and trimming) and slightly more complicated pattern B (same, plus vertical flip, binary formatting, and edge emphasis). This study used the VGG16 convolutional neural network. Pattern B machine learning was more accurate than pattern A. In our early model, Pattern A achieved 0.62 for accuracy, 0.56 for precision, 0.94 for recall, and 0.71 for F1 score. As for Pattern B, the scores were 0.79, 0.77, 0.96, and 0.86, respectively. Machine learning was sufficiently accurate to distinguish foot images between feet with hallux valgus and normal feet. With further refinement, this tool could be used for the easy screening of hallux valgus.

Assistive motion for sit-to-stand causes lower back pain (LBP) among caregivers. Considering previous studies that showed that foot position adjustment could reduce lumbar load during assistive motion for sit-to-stand, quantitative monitoring of and instructions on foot position could contribute toward reducing LBP among caregivers. The present study proposes and evaluates a new method for the quantitative measurement of foot position during assistive motion for sit-to-stand using a few wearable sensors that are not limited to the measurement area. The proposed method measures quantitative foot position (anteroposterior and mediolateral distance between both feet) through a machine learning technique using features obtained from only a single inertial sensor on the trunk and shoe-type force sensors. During the experiment, the accuracy of the proposed method was investigated by comparing the obtained values with those from an optical motion capture system. The results showed that the proposed method produced only minor errors (less than 6.5% of body height) when measuring foot position during assistive motion for sit-to-stand. Furthermore, Bland–Altman plots suggested no fixed errors between the proposed method and the optical motion capture system. These results suggest that the proposed method could be utilized for measuring foot position during assistive motion for sit-to-stand.

It is yet not known whether the variation in knee orthotics pressure would lead to changes in muscle activity during-sit-to-stand postural transition in patients with knee osteoarthritis (OA). Participants in this analytical study were patients with knee OA. The research design was a cross-sectional study. They were enrolled in the study through a sample of convenience method. The primary outcome measure was surface electromyography for measuring muscle activity while changing knee orthotics pressure during sit-to-stand motion. Data were summarized with mean and standard deviation while Friedman’s test was performed for multiple comparison of variables, at a significance level of p = 0.05. Seven elderly patients with knee osteoarthritis (mean age 71.4 ± 11.8 years) participated in the study. Moderate orthotics (7.3 mmHg) led to a significant increase in the percentage maximum voluntary contraction (MVC) of tibialis anterior compared to that obtained without orthotics. Rectus femoris, vastus medialis, vastus lateralis, and biceps femoris tended to increase the % MVC with an increase in wearing pressure. It was therefore concluded that the muscle activity during sit-to-stand motion could be increased in patients with knee osteoarthritis by wearing flexible orthotics with varying pressure.

The evolution of Electronic Health Records (EHR) has facilitated comprehensive patient record-keeping, enhancing healthcare delivery and decision-making processes. Despite these advancements, analyzing EHR data using ensemble machine learning methods poses unique challenges. These challenges include data dimensionality, imbalanced class distributions, and the need for effective hyperparameter tuning to optimize model performance. The study conducted a thorough comparative analysis of various ensemble machine learning (EML) models using Electronic Health Record (EHR) datasets. After addressing data imbalance and reducing dimensionality, the accuracy of the EML models showed significant improvement. Notably, the Gradient Boosting Machine (GBM) and CatBoost models exhibited superior performance with an accuracy of 73%, achieved through experiments involving dimensionality reduction and handling of imbalanced data. Furthermore, optimization techniques such as Grid Search and Random Search were employed to enhance the EML models. The results of model optimization revealed that the GBM + Random Search model performed the best, achieving an accuracy of 74%, followed by the XGBoost + Grid Search model with an accuracy of 73%. The GBM model also excelled in distinguishing between positive and negative classes, boasting the highest Area under Curve (AUC) value of 0.78, indicative of its superior classification capabilities compared to other models. This study emphasizes the significance of incorporating cutting-edge EML techniques into clinical workflows and emphasizes the revolutionary potential of GBM in classification modeling for patient severity conditions. Future research should focus on deep learning (DL) applications and the integration of these models.

[Purpose] In caregivers, low load posture is necessary to prevent lower back pain during patient handling activities such as sit-to-stand support. This study focused on the foot-position of caregivers as an adjustable and useful parameter. A wide stance decreases the stress on the lumbar vertebra. However, this foot-position increases loading of the spinae erector muscles. The aim of this study was to investigate the relationship of anterior-posterior and lateral-medial distances between feet and activity of the spinae erector muscles to determine the optimal foot-position for reducing stress on the lumbar vertebra without increasing spinae erector muscle load. [Participants and Methods] Five young male participants were asked to provide sit-to-stand support 10 times using nine normalized foot-positions with different anterior-posterior and lateral-medial distances. Surface electromyograms of the erector spinae and lower limb muscles were measured during sit-to-stand support. [Results] The results showed that the optimal foot-position (anterior-posterior 55%, lateral-medial 20% of body height) increased muscle activity within the lower limb muscles compared with the lower back muscles and did not increase loads on the erector spinae muscle. [Conclusion] Optimizing foot-position can reduce stress on the lumbar vertebra without increasing load on the spinae erector muscles.