AIMC Topic: Accidental Falls

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Belmont village senior living comprehensive fall management using new artificial intelligence program in memory care.

Geriatric nursing (New York, N.Y.) Nov 5, 2021
Humans Artificial Intelligence Accidental Falls
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Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults.

Sensors (Basel, Switzerland) Oct 28, 2021
Falls are a major cause of morbidity and mortality in neurological disorders. Technical means of detecting falls are of high interest as they enable rapid notification of caregivers and emergency services. Such approaches must reliably differentiate ...
Aged Humans Support Vector Machine Machine Learning Accidental Falls Activities of Daily Living Algorithms
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A Class-Imbalanced Deep Learning Fall Detection Algorithm Using Wearable Sensors.

Sensors (Basel, Switzerland) Sep 29, 2021
Falling represents one of the most serious health risks for elderly people; it may cause irreversible injuries if the individual cannot obtain timely treatment after the fall happens. Therefore, timely and accurate fall detection algorithm research i...
Accidental Falls Wearable Electronic Devices Seasons Aged Humans Deep Learning Algorithms
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A Soft Robotic Intervention for Gait Enhancement in Older Adults.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Sep 14, 2021
Falls continue to be a major safety and health concern for older adults. Researchers reported that increased gait variability was associated with increased fall risks. In the present study, we proposed a novel wearable soft robotic intervention and e...
Exercise Test Accidental Falls Gait Humans Walking Robotics Aged
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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 ...
Accidental Falls Machine Learning Deep Learning Humans
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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...
Support Vector Machine Male Accidental Falls Humans Neural Networks, Computer Machine Learning Algorithms
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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...
Gait Analysis Walking Speed Walking Gait Prognosis Aged Machine Learning Middle Aged Software Accidental Falls Aged, 80 and over Humans
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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...
Humans Accidental Falls Human Activities Neural Networks, Computer Aged
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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...
Accidental Falls Wearable Electronic Devices Walking Gait Retrospective Studies Deep Learning Humans Prospective Studies Multiple Sclerosis
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