AIMC Topic: Accidental Falls
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Identifying Falls Risk Screenings Not Documented with Administrative Codes Using Natural Language Processing.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
Quality reporting that relies on coded administrative data alone may not completely and accurately depict providers' performance. To assess this concern with a test case, we developed and evaluated a natural language processing (NLP) approach to iden...
Deep Learning for Fall Detection: Three-Dimensional CNN Combined With LSTM on Video Kinematic Data.
IEEE journal of biomedical and health informatics
Feb 20, 2018
Fall detection is an important public healthcare problem. Timely detection could enable instant delivery of medical service to the injured. A popular nonintrusive solution for fall detection is based on videos obtained through ambient camera, and the...
On the Comparison of Wearable Sensor Data Fusion to a Single Sensor Machine Learning Technique in Fall Detection.
Sensors (Basel, Switzerland)
Feb 14, 2018
In the context of the ageing global population, researchers and scientists have tried to find solutions to many challenges faced by older people. Falls, the leading cause of injury among elderly, are usually severe enough to require immediate medical...
An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection.
Sensors (Basel, Switzerland)
Dec 22, 2017
The fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors. However, th...
A robotic system for delivering novel real-time, movement dependent perturbations.
Gait & posture
Sep 1, 2017
Perturbations are often used to study movement control and balance, especially in the context of falling. Most often, discrete perturbations defined prior to the experiment are used to mimic external disturbances to balance. However, the largest prop...
Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets.
PloS one
Jul 5, 2017
Falls are a major cause of injuries and deaths in older adults. Even when no injury occurs, about half of all older adults who fall are unable to get up without assistance. The extended period of lying on the floor often leads to medical complication...
Feature selection for elderly faller classification based on wearable sensors.
Journal of neuroengineering and rehabilitation
May 30, 2017
BACKGROUND: Wearable sensors can be used to derive numerous gait pattern features for elderly fall risk and faller classification; however, an appropriate feature set is required to avoid high computational costs and the inclusion of irrelevant featu...
A Combined One-Class SVM and Template-Matching Approach for User-Aided Human Fall Detection by Means of Floor Acoustic Features.
Computational intelligence and neuroscience
May 30, 2017
The primary cause of injury-related death for the elders is represented by falls. The scientific community devoted them particular attention, since injuries can be limited by an early detection of the event. The solution proposed in this paper is bas...
Continuous detection of human fall using multimodal features from Kinect sensors in scalable environment.
Computer methods and programs in biomedicine
May 25, 2017
BACKGROUND AND OBJECTIVES: Automatic detection of human fall is a key problem in video surveillance and home monitoring. Existing methods using unimodal data (RGB / depth / skeleton) may suffer from the drawbacks of inadequate lighting condition or u...
Artificial neural networks: Predicting head CT findings in elderly patients presenting with minor head injury after a fall.
The American journal of emergency medicine
Nov 3, 2016
OBJECTIVES: To construct an artificial neural network (ANN) model that can predict the presence of acute CT findings with both high sensitivity and high specificity when applied to the population of patients≥age 65years who have incurred minor head i...
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