Background: Gait is the manner or style of walking, involving motor control and coordination to adapt to the surrounding environment. Knowing the kinesthetic markers of normal gait is essential for the diagnosis of certain pathologies or the generation of intelligent ortho-prostheses for the treatment or prevention of gait disorders. The aim of the present study was to identify the key features of normal human gait using inertial unit (IMU) recordings in a walking test. Methods: Gait analysis was conducted on 32 healthy participants (age range 19–29 years) at speeds of 2 km/h and 4 km/h using a treadmill. Dynamic data were obtained using a microcontroller (Arduino Nano 33 BLE Sense Rev2) with IMU sensors (BMI270). The collected data were processed and analyzed using a custom script (MATLAB 2022b), including the labeling of the four relevant gait phases and events (Stance, Toe-Off, Swing, and Heel Strike), computation of statistical features (64 features), and application of machine learning techniques for classification (8 classifiers). Results: Spider plot analysis revealed significant differences in the four events created by the most relevant statistical features. Among the different classifiers tested, the Support Vector Machine (SVM) model using a Cubic kernel achieved an accuracy rate of 92.4% when differentiating between gait events using the computed statistical features. Conclusions: This study identifies the optimal features of acceleration and gyroscope data during normal gait. The findings suggest potential applications for injury prevention and performance optimization in individuals engaged in activities involving normal gait. The creation of spider plots is proposed to obtain a personalised fingerprint of each patient’s gait fingerprint that could be used as a diagnostic tool. A deviation from a normal gait pattern can be used to identify human gait disorders. Moving forward, this information has potential for use in clinical applications in the diagnosis of gait-related disorders and developing novel orthoses and prosthetics to prevent falls and ankle sprains.

In the literature, many researchers investigated static loading effects on an implant. However, dynamic loading under impact loading has not been investigated formally using numerical methods. This study aims to evaluate, with 3D finite element analysis (3D FEA), the stress transferred (maximum peak and variation in time) from a dynamic impact force applied to a single implant-supported prosthesis made from different materials. A 3D implant-supported prosthesis model was created on a digital model of a mandible section using CAD and reverse engineering. By setting different mechanical properties, six implant-supported prostheses made from different materials were simulated: metal (MET), metal-ceramic (MCER), metal-composite (MCOM), carbon fiber-composite (FCOM), PEEK-composite (PKCOM), and carbon fiber-ceramic (FCCER). Three-dimensional FEA was conducted to simulate the collision of 8.62 g implant-supported prosthesis models with a rigid plate at a speed of 1 m/s after a displacement of 0.01 mm. The stress peak transferred to the crown, titanium abutment, and cortical bone, and the stress variation in time, were assessed.

Statement of problem. Previous peri-implantitis, peri-implant bone regeneration, or immediate implant placement postextraction may be responsible for the absence of cortical bone. Single crown materials are then relevant when dynamic forces are transferred into bone tissue and, therefore, the presence (or absence) of cortical bone can affect the long-term survival of the implant. Purpose: the purpose of this study is to assess the biomechanical response of dental rehabilitation when selecting different crown materials in models with and without cortical bone. Methods: several crown materials were considered for modeling six types of crown rehabilitation: full metal (MET), metal-ceramic (MCER), metal-composite (MCOM), peek-composite (PKCOM), carbon fiber-composite (FCOM), and carbon fiber-ceramic (FCCER). An impact-load dynamic finite-element analysis was carried out on all the 3D models of crowns mentioned above to assess their mechanical behavior against dynamic excitation. Implant-crown rehabilitation models with and without cortical bone were analyzed to compare how the load-impact actions affect both type of models. Results: numerical simulation results showed important differences in bone tissue stresses. The results show that flexible restorative materials reduce the stress on the bone and would be especially recommendable in the absence of cortical bone. Conclusions: this study demonstrated that more stress is transferred to the bone when stiffer materials (metal and/or ceramic) are used in implant supported rehabilitations; conversely, more flexible materials transfer less stress to the implant connection. Also, in implant-supported rehabilitations, more stress is transferred to the bone by dynamic forces when cortical bone is absent.

As its name implies, the forward head position (FHP) is when the head is further forward of the trunk than normal. This can cause neck and shoulder tension, as well as headaches. The craniovertebral angle (CVA) measured with 2D systems such as Kinovea software is often used to assess the FHP. Computer vision applications have proven to be reliable in different areas of daily life. The aim of this study is to analyze the test-retest and inter-rater reliability and the concurrent validity of a smartphone application based on computer vision for the measurement of the CVA. Methods: The CVAs of fourteen healthy volunteers, fourteen neck pain patients, and fourteen tension-type headache patients were assessed. The assessment was carried out twice, with a week of rest between sessions. Each examiner took a lateral photo in a standing position with the smartphone app based on computer vision. The test-retest reliability was calculated with the assessment of the CVA measured by the smartphone application, and the inter-rater reliability was also calculated. A third examiner assessed the CVA using 2D Kinovea software to calculate its concurrent validity. Results: The CVA in healthy volunteers was 54.65 (7.00); in patients with neck pain, 57.67 (5.72); and in patients with tension-type headaches, 54.63 (6.48). The test-retest reliability was excellent, showing an Intraclass Correlation Coefficient (ICC) of 0.92 (0.86–0.95) for the whole sample. The inter-rater reliability was excellent, with an ICC of 0.91 (0.84–0.95) for the whole sample. The standard error of the measurement with the app was stated as 1.83°, and the minimum detectable change was stated as 5.07°. The concurrent validity was high: r = 0.94, p < 0.001. Conclusion: The computer-based smartphone app showed excellent test-retest and inter-rater reliability and strong concurrent validity compared to Kinovea software for the measurement of CVA.

Dental implants offer an effective solution for partial and total edentulism, but mechanical and biological complications exist. Furthermore, high occlusal loads challenge implants and lead to potential failures. This review focuses on impact testing in contrast to incremental and static tests, an underexplored aspect of assessing daily loads on implants, bringing to light potential complications. The review examines studies employing impact forces to assess implant-supported prostheses and natural teeth properties, highlighting their significance in dental research. A systematic search following PRISMA guidelines identified 21 relevant articles out of 224, emphasizing studies employing impact forces to evaluate various aspects of dental implant treatments. The diverse applications of impact forces in dental research were categorized into tooth structure, restorative materials, interface evaluation, implant properties, and finite element models. Some studies showed the significance of impact forces in assessing stress distribution, shock absorption, and biomechanical response. Impact testing is a critical tool for understanding the daily forces on implants. Despite diverse experimental approaches, a lack of standardized protocols complicates the systematization of the results and, therefore, the conclusions. This review highlights the need for consistent methodologies in impact testing studies for future research on implant-supported prostheses.