Three-dimensional (3D) surface scanning technologies are being used to produce digitized representation of human anatomy parts. The purpose of this study was to determine the agreement between two 3D scanners (Structure iSense and TrueDepth iPhone X) to obtaining anthropometric data from the foot. Fourteen feet from seven voluntary healthy adults (four males and three females) were scanned. 3D landmarks were placed on 1st metatarsal head (1MTH), 5th metatarsal head (5MTH), heel (H) and on the plantar side of the scaphoid tubercle (ST) to acquire measurements in the three space-axes (X, Y and Z). Intraclass correlation coefficient (ICC) > 0.9 was obtained for Structure iSense for the measurements taken in the three axes. ICC > 0.9 was obtained TrueDepth iPhone X for X- and Y-axes and ICC < 0.75 for Z-axis. Low standard error measure (SEM) was obtained in both scanners. The two methods provide moderate to excellent reliability measuring the foot in the three space-axes.

Background/Objectives: Sarcopenia and cognitive decline (CD) are prevalent in aging populations, impacting functionality and quality of life. The early detection of these diseases is challenging, often relying on in-person screening, which is difficult to implement regularly. This study aims to develop artificial intelligence algorithms based on gait analysis, integrating sensor and computer vision (CV) data, to detect sarcopenia and CD. Methods: A cross-sectional case-control study was conducted involving 42 individuals aged 60 years or older. Participants were classified as having sarcopenia if they met the criteria established by the European Working Group on Sarcopenia in Older People and as having CD if their score in the Mini-Mental State Examination was ≤24 points. Gait patterns were assessed at usual walking speeds using sensors attached to the feet and lumbar region, and CV data were captured using a camera. Several key variables related to gait dynamics were extracted. Finally, machine learning models were developed using these variables to predict sarcopenia and CD. Results: Models based on sensor data, CV data, and a combination of both technologies achieved high predictive accuracy, particularly for CD. The best model for CD achieved an F1-score of 0.914, with a 95% sensitivity and 92% specificity. The combined technologies model for sarcopenia also demonstrated high performance, yielding an F1-score of 0.748 with a 100% sensitivity and 83% specificity. Conclusions: The study demonstrates that gait analysis through sensor and CV fusion can effectively screen for sarcopenia and CD. The multimodal approach enhances model accuracy, potentially supporting early disease detection and intervention in home settings.

Gait variables assessed by inertial measurement units (IMUs) show promise as screening tools for aging-related diseases like sarcopenia. The main aims of this systematic review were to analyze and synthesize the scientific evidence for screening sarcopenia based on gait variables assessed by IMUs, and also to review articles that investigated which gait variables assessed by IMUs were related to sarcopenia. Six electronic databases (PubMed, SportDiscus, Web of Science, Cochrane Library, Scopus and IEEE Xplore) were searched for journal articles related to gait, IMUs and sarcopenia. The search was conducted until December 5, 2023. Titles, abstracts and full-length texts for studies were screened to be included. A total of seven articles were finally included in this review. Despite some methodological variability among the included studies, IMUs demonstrated potential as effective tools for detecting sarcopenia when coupled with artificial intelligence (AI) models, which outperformed traditional statistical methods in classification accuracy. The findings suggest that gait variables related to the stance phase such as stance duration, double support time, and variations between feet, are key indicators of sarcopenia. IMUs could be useful tools for sarcopenia screening based on gait analysis, specifically when artificial intelligence is used to process the recorded data. However, more development and research in this field is needed to provide an effective screening tool for doctors and health systems.

The aim of the present research was to determine the effectiveness of relieving calcaneal apophysitis pain using “off-the-shelf” heel-lifts and custom-made orthotics. Two intervention modalities were evaluated and compared in a 12-week follow-up trial. Inclusion criteria included 9- to 12-year-old children diagnosed with calcaneal apophysitis. Children were randomly stratified into treatment A (custom-made polypropylene foot orthoses) and treatment B (“off-the-shelf” heel-lifts) groups. Treatment effectiveness was measured by algometry and the visual analogical scale (VAS). A total of 208 patients were included. The treatment A group showed an increase in threshold algometry of 53.4% (95% CI 47.1% to 59.7%) and a decrease in VAS of −68.6% (95% CI −74.5% to −62.7%) compared with the treatment B group (p < 0.001). Calcaneal apophysitis pain perception was improved in both groups, but children who used custom-made foot orthoses showed a greater improvement.