Query: risk of fall computer vision

title: Measuring Gait Variables Using Computer Vision to Assess Mobility and Fall Risk in Older Adults With Dementia
abstract: Fall risk is high for older adults with dementia. Gait impairment contributes to increased fall risk, and gait changes are common in people with dementia, although the reliable assessment of gait is challenging in this population. This study aimed to develop an automated approach to performing gait assessments based on gait data that is collected frequently and unobtrusively, and analysed using computer vision methods. Recent developments in computer vision have led to the availability of open source human pose estimation algorithms, which automatically estimate the joint locations of a person in an image. In this study, a pre existing pose estimation model was applied to 1066 walking videos collected of 31 older adults with dementia as they walked naturally in a corridor on a specialized dementia unit over a two week period. Using the tracked pose information, gait features were extracted from video recordings of gait bouts and their association with clinical mobility assessment scores and future falls data was examined. A significant association was found between extracted gait features and a clinical mobility assessment and the number of future falls, providing concurrent and predictive validation of this approach.

title: Computer Vision and Abnormal Patient Gait: A Comparison of Methods
abstract: Abnormal gait, falls and its associated complications have high morbidity and mortality. Computer vision detects, predicts gait abnormalities, assesses fall risk, and serves as a clinical decision support tool for physicians. This paper performs a systematic review of computer vision, machine learning techniques to analyse abnormal gait. This literature outlines the use of different machine learning and poses estimation algorithms in gait analysis that includes partial affinity fields, pictorial structures model, hierarchical models, sequential prediction frameworkbased approaches, convolutional pose machines, gait energy image, 2 Directional 2 dimensional principles component analysis (2D) 2PCA) and 2G (2D) 2PCA) Enhanced Gait Energy Image (EGEI) SVM, ANN, K Star, Random Forest, KNN, to perform the image classification of the features extracted inpatient gait abnormalities.

title: Computer Vision and Abnormal Patient Gait Assessment a Comparison of Machine Learning Models
abstract: Abnormal gait, its associated falls and complications have high patient morbidity, mortality. Computer vision detects, predicts patient gait abnormalities, assesses fall risk and serves as clinical decision support tool for physicians. This paper performs a systematic review of how computer vision, machine learning models perform an abnormal patient's gait assessment. Computer vision is beneficial in gait analysis, it helps capture the patient posture. Several literature suggests the use of different machine learning algorithms such as SVM, ANN, K Star, Random Forest, KNN, among others to perform the classification on the features extracted to study patient gait abnormalities.

title: Automated Depth Video Monitoring For Fall Reduction A Case Study
abstract: Patient falls are a common, costly, and serious safety problem in hospitals and healthcare facilities. We have created a system that reduces falls by using computer vision to monitor fall risk patients and alert staff of unsafe behavior before a fall happens. This paper is a companion and followup to "Modeling bed exit likelihood in a camera based automated video monitoring application," in which we describe the Ocuvera system.  Here additional details are provided on that system and its processes. We report clinical results, detail practices used to iterate rapidly and effectively on a massive video database, discuss details of our people tracking algorithms, and discuss the engineering effort required to support the new Azure Kinect depth camera.

title: A Deep Learning Approach Towards Railway Safety Risk Assessment
abstract: Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks.

title: Fall detection based on posture classification for smart home environment
abstract: Automatic human fall detection is one of the major components in the elderly monitoring system. Detection of human fall in a smart home environment can be utilized as a means to improve the quality of care offered to elderly people thus reducing the risk factor when they are alone. Recently various fall detection approaches have been proposed, among which computer vision based approaches offer promising and effective solutions. In this paper, an analysis of fall detection is carrier out based on the automatic, feature learning in a hybrid approach. Initially, a model is generated using the training dataset that contains samples of both fall and normal active events. Then key frames are extracted from the video sequence that is subjected to two stream classification. The classification results are approved if both the streams project the same results, failing so, additional information are used to classify the fall from the normal activity. The selection of key frames depends on the displacement in the centroid of the detected object have threshold greater than the predefined value. Experiments show that the proposed approach achieves reliable results compared with other methods, and a better result is achieved in our method even when training with fewer training samples.

title: Predicting TUG score from gait characteristics based on video analysis and machine learning
abstract: Fall is a leading cause of death which suffers the elderly and society. Timed Up and Go (TUG) test is a common tool for fall risk assessment. In this paper, we propose a method for predicting TUG score from gait characteristics extracted from video with computer vision and machine learning technologies. First, 3D pose is estimated from video captured with 2D and 3D cameras during human motion and then a group of gait characteristics are computed from 3D pose series. After that, copula entropy is used to select those characteristics which are mostly associated with TUG score. Finally, the selected characteristics are fed into the predictive models to predict TUG score. Experiments on real world data demonstrated the effectiveness of the proposed method. As a byproduct, the associations between TUG score and several gait characteristics are discovered, which laid the scientific foundation of the proposed method and make the predictive models such built interpretable to clinical users.

title: Predicting TUG score from gait characteristics with video analysis and machine learning
abstract: Fall is a leading cause of death which suffers the elderly and society. Timed Up and Go (TUG) test is a common tool for fall risk assessment. In this paper, we propose a method for predicting TUG score from gait characteristics extracted from video with computer vision and machine learning technologies. First, 3D pose is estimated from video captured with 2D and 3D cameras during human motion and then a group of gait characteristics are computed from 3D pose series. After that, copula entropy is used to select those characteristics which are mostly associated with TUG score. Finally, the selected characteristics are fed into the predictive models to predict TUG score. Experiments on real world data demonstrated the effectiveness of the proposed method. As a byproduct, the associations between TUG score and several gait characteristics are discovered, which laid the scientific foundation of the proposed method and make the predictive models such built interpretable to clinical users.

title: NeuroPose: geriatric rehabilitation in the home using a webcam and pose estimation
abstract: Many chronic diseases and common risks to elderly patients can be assessed and treated through standardized training and rehabilitation programs. Unfortunately, there is a need to make risk assessment and preventative care for the elderly more easily accessible as many programs either use specialized hardware or require human supervision. We seek to reduce the barrier to entry for patients through a portable application which enables fall risk prevention assessment and rehabilitation anywhere. Our work leverages the latest in machine learning and computer vision, accomplishing pose estimation and body tracking with a simple and ubiquitous web cam. Thus patients can be screened anywhere with the ability to get feedback in near real time.

title: An open vibration and pressure platform for fall prevention with a reinforcement learning agent
abstract: The risk of falls among the elderly population is one that may lead to dire consequences. It can significantly affect the quality of life of the victims and even lead to their premature death. Many technological tools have been proposed in the literature to detect falls, but little effort has been done regarding their prevention. In this paper, our research team proposes an inexpensive open vibration platform equipped with pressure sensors. The platform is built from easily available electronic components to be used as a tool by physiotherapists in order to help them in their evaluation of the postural control of individuals at risk of postural imbalance. The platform has been built to be easily reproducible by the scientific community. Moreover, the computer code necessary to make it work is fully open source and can be used in any non commercial applications. A first version of the platform was tested with 7 healthy human participants. A simple reinforcement learning agent was deployed and tested to automatically calibrate the vibration motors for optimal stimulation. The agent exploited computer vision to capture the data from a force platform commercially available and use it as ground truth. Finally, a second version of the platform was built and is presented in the paper. That version is currently being validated clinically with both healthy and impaired human participants. The preliminary data are presented in this paper.