AIMC Topic: Accidental Falls

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Human inspired fall arrest strategy for humanoid robots based on stiffness ellipsoid optimisation.

Bioinspiration & biomimetics Aug 23, 2021
Falls are a common risk and impose severe threats to both humans and humanoid robots as a product of bipedal locomotion. Inspired by human fall arrest, we present a novel humanoid robot fall prevention strategy by using arms to make contact with envi...
Humans Torque Locomotion Upper Extremity Accidental Falls Robotics
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Domain-Adaptive Fall Detection Using Deep Adversarial Training.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jul 12, 2021
Fall detection (FD) systems are important assistive technologies for healthcare that can detect emergency fall events and alert caregivers. However, it is not easy to obtain large-scale annotated fall events with various specifications of sensors or ...
Deep Learning Humans Accidental Falls Machine Learning
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The Performance of Post-Fall Detection Using the Cross-Dataset: Feature Vectors, Classifiers and Processing Conditions.

Sensors (Basel, Switzerland) Jul 6, 2021
In this study, algorithms to detect post-falls were evaluated using the cross-dataset according to feature vectors (time-series and discrete data), classifiers (ANN and SVM), and four different processing conditions (normalization, equalization, incr...
Accidental Falls Support Vector Machine Algorithms Neural Networks, Computer Humans Machine Learning Male
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XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes.

Scientific reports Jun 9, 2021
This study aimed to identify the optimal features of gait parameters to predict the fall risk level in older adults. The study included 746 older adults (age: 63-89 years). Gait tests (20 m walkway) included speed modification (slower, preferred, and...
Humans Prognosis Aged Aged, 80 and over Machine Learning Software Gait Analysis Walking Speed Walking Gait Middle Aged Accidental Falls
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High Accuracy WiFi-Based Human Activity Classification System with Time-Frequency Diagram CNN Method for Different Places.

Sensors (Basel, Switzerland) May 30, 2021
Older people are very likely to fall, which is a significant threat to the health. However, falls are preventable and are not necessarily an inevitable part of aging. Many different fall detection systems have been developed to help people avoid fall...
Human Activities Aged Accidental Falls Humans Neural Networks, Computer
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Wearables and Deep Learning Classify Fall Risk From Gait in Multiple Sclerosis.

IEEE journal of biomedical and health informatics May 11, 2021
Falls are a significant problem for persons with multiple sclerosis (PwMS). Yet fall prevention interventions are not often prescribed until after a fall has been reported to a healthcare provider. While still nascent, objective fall risk assessments...
Multiple Sclerosis Walking Gait Prospective Studies Retrospective Studies Accidental Falls Wearable Electronic Devices Deep Learning Humans
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Revealing posturographic profile of patients with Parkinsonian syndromes through a novel hypothesis testing framework based on machine learning.

PloS one Feb 25, 2021
Falling in Parkinsonian syndromes (PS) is associated with postural instability and consists a common cause of disability among PS patients. Current posturographic practices record the body's center-of-pressure displacement (statokinesigram) while the...
Models, Neurological Humans Postural Balance Male Parkinsonian Disorders Aged Female Machine Learning Accidental Falls Aged, 80 and over
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Comprehensive Review of Vision-Based Fall Detection Systems.

Sensors (Basel, Switzerland) Feb 1, 2021
Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the m...
Accidental Falls Neural Networks, Computer Gait Lighting Humans Databases, Factual Aged
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A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection.

Sensors (Basel, Switzerland) Jan 30, 2021
Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) usi...
Activities of Daily Living Machine Learning Algorithms Female Accidental Falls Aged Humans Wearable Electronic Devices Male
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An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box.

Scientific reports Jan 28, 2021
Falls are a leading cause of unintentional injuries and can result in devastating disabilities and fatalities when left undetected and not treated in time. Current detection methods have one or more of the following problems: frequent battery replace...
Humans Accidental Falls Activities of Daily Living Machine Learning Accelerometry
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