Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction

Background: Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge for the clinician. How to realize automatic identification and judgment of abnormal gait is a significant challenge in clinical practice. The long-term goal of our study is to develop a gait recognition computer vision system using artificial intelligence (AI) and machine learning (ML) computing. This study aims to find an optimal ML algorithm using computer vision techniques and measure variables from lower limbs to classify gait patterns in healthy people. The purpose of this study is to determine the feasibility of computer vision and machine learning (ML) computing in discriminating different gait patterns associated with flat-ground falls. Methods: We used the Kinect® Motion system to capture the spatiotemporal gait data from seven healthy subjects in three walking trials, including normal gait, pelvic-obliquity-gait, and knee-hyperextension-gait walking. Four different classification methods including convolutional neural network (CNN), support vector machine (SVM), K-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks were used to automatically classify three gait patterns. Overall, 750 sets of data were collected, and the dataset was divided into 80% for algorithm training and 20% for evaluation. Results: The SVM and KNN had a higher accuracy than CNN and LSTM. The SVM (94.9 ± 3.36%) had the highest accuracy in the classification of gait patterns, followed by KNN (94.0 ± 4.22%). The accuracy of CNN was 87.6 ± 7.50% and that of LSTM 83.6 ± 5.35%. Conclusions: This study revealed that the proposed AI machine learning (ML) techniques can be used to design gait biometric systems and machine vision for gait pattern recognition. Potentially, this method can be used to remotely evaluate elderly patients and help clinicians make decisions regarding disposition, follow-up, and treatment

Understanding the Burden of Traumatic Injuries at the United States-Mexico Border: A Scoping Review of the Literature

The United States-Mexico border is the busiest land crossing in the world and faces continuously increasing numbers of undocumented border crossers. Significant barriers to crossing are present in many regions of the border, including walls, bridges, rivers, canals, and the desert, each with unique features that can cause traumatic injury. The number of patients injured attempting to cross the border is also increasing, but significant knowledge gaps regarding these injuries and their impacts remain. The purpose of this scoping literature review is to describe the current state of trauma related to the US–Mexico border to draw attention to the problem, identify knowledge gaps in the existing literature, and introduce the creation of a consortium made up of representatives from border trauma centers in the Southwestern United States, the Border Region Doing Research on Trauma (BRDR-T) Consortium. Consortium members will collaborate to produce multicenter, up-to-date data on the medical impact of the US-Mexico border, helping to elucidate the true magnitude of the problem and shed light on the impact cross-border trauma has on migrants, their families, and the United States healthcare system. Only once the problem is fully described can meaningful solutions be provided