Evaluation of human gait through smartphone-based pose estimation algorithms provides an attractive alternative to costly lab-bound instrumented assessment and offers a paradigm shift with real time gait capture for clinical assessment. Systems based on smart phones, such as OpenPose and BlazePose have demonstrated potential for virtual motion assessment but still lack the accuracy and repeatability standards required for clinical viability. Seq2seq architecture offers an alternative solution to conventional deep learning techniques for predicting joint kinematics during gait. This study introduces a novel enhancement to the low-powered BlazePose algorithm by incorporating a Seq2seq autoencoder deep learning model. To ensure data accuracy and reliability, synchronized motion capture involving an RGB camera and ten Vicon cameras were employed across three distinct self-selected walking speeds. This investigation presents a groundbreaking avenue for remote gait assessment, harnessing the potential of Seq2seq architectures inspired by natural language processing (NLP) to enhance pose estimation accuracy. When comparing BlazePose alone to the combination of BlazePose and 1D convolution Long Short-term Memory Network (1D-LSTM), Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), the average mean absolute errors decreased from 13.4° to 5.3° for fast gait, from 16.3° to 7.5° for normal gait, and from 15.5° to 7.5° for slow gait at the left ankle joint angle respectively. The strategic utilization of synchronized data and rigorous testing methodologies further bolsters the robustness and credibility of these findings.

Bone and Site Hold Tendon Inside (BASHTI) technique is an organic implant-less technique for anterior cruciate ligament (ACL) reconstruction with some clinical advantages, such as speeding up the healing process, over implantable techniques. The study aims to compare the mechanical properties of BASHTI technique with the conventional interference screw technique. To investigate the mechanical properties, 20 in-vitro experimental tests were conducted. Synthetic dummy bone, along with fresh digital bovine tendons, as a graft, were used for experiments. Three loading steps were applied to all specimens, including a preconditioning, a main cyclic, and a pull-out loading. The mechanical characters of an interference screw technique using an 8 mm tendon diameter, including fixation strength, average cyclic stiffness (ACS), and average pull-out stiffness (APS) were found to be 439 ± 132 N, 10.3 ± 5.3 kN/mm, and 109 ± 40 N/mm, respectively. In the case of an interference screw using a 9 mm tendon, the fixation strength, ACS, and APS were obtained 549 ± 87 N, 10.3 ± 4.7 kN/mm, and 91 ± 13 N/mm, respectively. In parallel, the fixation strength, APS, and ACS of BASHTI technique using an 8 mm tendon were 360 ± 123 N, 3.3 ± 0.6 kN/mm, and 79 ± 27 N/mm, respectively, while, for 9 mm tendon 278 ± 103 N, 2.4 ± 1.2 kN/mm, and 111 ± 40 N/mm, were reported for fixation strength, APS, and ACS respectively when BASHTI technique was used. About 50% of interference screw samples showed superior mechanical properties compared to BASHTI technique, but in another half of the samples, the differences were not significant (N.S.). However, due to organic advantages of BASHTI technique and lower cost, it could be used as a substitute for interference screw technique, especially where fast recovery is expected.

Accurate and user-friendly joint kinematic measurement is essential in clinical gait assessment. Pose estimation algorithms offer an alternative to cumbersome marker-based motion capture, whether optical or IMU-based. This study assesses AlphaPose and BlazePose pose estimation tools efficacy in determining gait joint kinematics against Vicon motion capture. Ten healthy male participants walked at varied speeds, with concurrent measurements via thirty-nine reflective skin markers and a GoPro RGB camera in sagittal plane. Pose estimation tools processed videos, and trigonometric calculations derived joint angles. BlazePose demonstrated lower root mean square error (RMSE) values than Vicon, with a maximum of 14.2° in the left knee during slow gait. The Wilcoxon signed-rank test revealed significant joint measurement differences, worsening with speed. Both AlphaPose and BlazePose differ in performance compared to Vicon. AlphaPose generally yielded higher ranges of motion (RoM) and larger RMSE values, while BlazePose exhibited elevated normalized RMSE values. With further improvements to BlazePose algorithm, clinicians will be empowered to conduct real-time pre- and post-intervention gait assessments effortlessly using standard cameras.