A 3D-Printed Knee Wearable Goniometer with a Mobile-App Interface for Measuring Range of Motion and Monitoring Activities

Empowering Rehabilitation: Design and Structural Analysis of a Low-Cost 3D-Printed Smart Orthosis

Three-dimensional (3D) printing of polymer materials encompasses a wide range of applications and innovations. Polymer-based 3D printing, also known as additive manufacturing, has gained significant attention due to its versatility, cost-effectiveness, and potential to revolutionize various industries. The current paper focuses on obtaining a durable low-cost rehabilitation knee orthosis. Researchers propose that the entire structure should be obtained using modern equipment within the additive manufacturing domain-3D printing. The researchers focus on determining, through a 3D analysis of the entire 3D model assembly, which parts present a high degree of stress when a kinematic simulation is developed. The entire 3D model of the orthosis starts based on the result obtained from a 3D scanning of the knee joint of a patient, providing a precise fixation, and allowing for direct personalization. Based on the results and identification of the critical parts, there will be used different materials and a combination of 3D printing strategies to validate the physical model of the entire orthosis. For the manufacturing process, the researchers use two types of low-cost fused filament fabrication (FFF), which are easy to find on the worldwide market. The motivation for manufacturing the entire assembly using 3D printing techniques is the short time in which complex shapes can be obtained, which is relevant for the present study. The main purpose of the present research is to advance orthotic technology by developing an innovative knee brace made of 3D-printed polymers that are designed to be lightweight, easy-to-use, and provide comfort and functionality to patients during the rehabilitation process.

Merging BioActuation and BioCapacitive properties: A 3D bioprinted devices to self-stimulate using self-stored energy

Biofabrication of three-dimensional (3D) cultures through the 3D Bioprinting technique opens new perspectives and applications of cell-laden hydrogels. However, to continue with the progress, new BioInks with specific properties must be carefully designed. In this study, we report the synthesis and 3D Bioprinting of an electroconductive BioInk made of gelatin/fibrinogen hydrogel, C2C12 mouse myoblast and 5% w/w of conductive poly (3,4-ethylenedioxythiophene) nanoparticles (PEDOT NPs). The influence of PEDOT NPs, incorporated in the cell-laden BioInk, not only showed a positive effect in cells viability, differentiation and myotube functionalities, also allowed the printed constructs to behaved as BioCapacitors. Such devices were able to electrochemically store a significant amount of energy (0.5 mF/cm2), enough to self-stimulate as BioActuator, with typical contractions ranging from 27 to 38 μN, during nearly 50 min. The biofabrication of 3D constructs with the proposed electroconductive BioInk could lead to new devices for tissue engineering, biohybrid robotics or bioelectronics.

The Difference in the Assessment of Knee Extension/Flexion Angles during Gait between Two Calibration Methods for Wearable Goniometer Sensors

Frontal and axial knee motion can affect the accuracy of the knee extension/flexion motion measurement using a wearable goniometer. The purpose of this study was to test the hypothesis that calibrating the goniometer on an individual's body would reduce errors in knee flexion angle during gait, compared to bench calibration. Ten young adults (23.2 ± 1.3 years) were enrolled. Knee flexion angles during gait were simultaneously assessed using a wearable goniometer sensor and an optical three-dimensional motion analysis system, and the absolute error (AE) between the two methods was calculated. The mean AE across a gait cycle was 2.4° (0.5°) for the on-body calibration, and the AE was acceptable (<5°) throughout a gait cycle (range: 1.5-3.8°). The mean AE for the on-bench calibration was 4.9° (3.4°) (range: 1.9-13.6°). Statistical parametric mapping (SPM) analysis revealed that the AE of the on-body calibration was significantly smaller than that of the on-bench calibration during 67-82% of the gait cycle. The results indicated that the on-body calibration of a goniometer sensor had acceptable and better validity compared to the on-bench calibration, especially for the swing phase of gait.

Validity of AI-Based Gait Analysis for Simultaneous Measurement of Bilateral Lower Limb Kinematics Using a Single Video Camera

Accuracy validation of gait analysis using pose estimation with artificial intelligence (AI) remains inadequate, particularly in objective assessments of absolute error and similarity of waveform patterns. This study aimed to clarify objective measures for absolute error and waveform pattern similarity in gait analysis using pose estimation AI (OpenPose). Additionally, we investigated the feasibility of simultaneous measuring both lower limbs using a single camera from one side. We compared motion analysis data from pose estimation AI using video footage that was synchronized with a three-dimensional motion analysis device. The comparisons involved mean absolute error (MAE) and the coefficient of multiple correlation (CMC) to compare the waveform pattern similarity. The MAE ranged from 2.3 to 3.1° on the camera side and from 3.1 to 4.1° on the opposite side, with slightly higher accuracy on the camera side. Moreover, the CMC ranged from 0.936 to 0.994 on the camera side and from 0.890 to 0.988 on the opposite side, indicating a "very good to excellent" waveform similarity. Gait analysis using a single camera revealed that the precision on both sides was sufficiently robust for clinical evaluation, while measurement accuracy was slightly superior on the camera side.

Validity and Reliability of a Wearable Goniometer Sensor Controlled by a Mobile Application for Measuring Knee Flexion/Extension Angle during the Gait Cycle

Knee kinematics during gait is an important assessment tool in health-promotion and clinical fields. This study aimed to determine the validity and reliability of a wearable goniometer sensor for measuring knee flexion angles throughout the gait cycle. Twenty-two and seventeen participants were enrolled in the validation and reliability study, respectively. The knee flexion angle during gait was assessed using a wearable goniometer sensor and a standard optical motion analysis system. The coefficient of multiple correlation (CMC) between the two measurement systems was 0.992 ± 0.008. Absolute error (AE) was 3.3 ± 1.5° (range: 1.3-6.2°) for the entire gait cycle. An acceptable AE (<5°) was observed during 0-65% and 87-100% of the gait cycle. Discrete analysis revealed a significant correlation between the two systems (R = 0.608-0.904, p ≤ 0.001). The CMC between the two measurement days with a 1-week interval was 0.988 ± 0.024, and the AE was 2.5 ± 1.2° (range: 1.1-4.5°). A good-to-acceptable AE (<5°) was observed throughout the gait cycle. These results indicate that the wearable goniometer sensor is useful for assessing knee flexion angle during the stance phase of the gait cycle.