AIMC Topic: Accidental Falls

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Characteristics of Transactive Relationship Phenomena among Older adults, Care Workers as Intermediaries, and the Pepper Robot with Care Prevention Gymnastics Exercises.

The journal of medical investigation : JMI Jan 1, 2019
Healthcare for older adults is a significant problem in Japan and in other developed countries. To address this problem, healthcare robots, now realized, can assist and meet healthcare and welfare practice demands. The aim of this study was to clarif...
Humans Accidental Falls Robotics Aged Gymnastics Exercise Health Personnel
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Co-Saliency-Enhanced Deep Recurrent Convolutional Networks for Human Fall Detection in E-Healthcare.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
This paper addresses the issue of fall detection from videos for e-healthcare and assisted-living. Instead of using conventional hand-crafted features from videos, we propose a fall detection scheme based on co-saliency-enhanced recurrent convolution...
Accidental Falls Video Recording Neural Networks, Computer Deep Learning Humans
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Design and analysis of a novel fall prevention device for lower limbs rehabilitation robot.

Journal of back and musculoskeletal rehabilitation Feb 6, 2018
BACKGROUND: Most stroke survivors are suffering from physical motor impairments and confronting with the risk of falls, and well trunk stability is essential for balance during daily functional activities.
Humans Female Aged Robotics Equipment Design Middle Aged Torso Accidental Falls Stroke Rehabilitation Lower Extremity Biomechanical Phenomena Male
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Can Staff Distinguish Falls: Experimental Hypothesis Verification Using Japanese Incident Reports and Natural Language Processing.

Studies in health technology and informatics Jan 1, 2018
Falls are generally classified into two groups in clinical settings in Japan: falls from the same level and falls from one level to another. We verified whether clinical staff could distinguish between these two types of falls by comparing 3,078 free...
Machine Learning Accidental Falls Natural Language Processing Humans Risk Management Japan
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Establishing a Classification System for High Fall-Risk Among Inpatients Using Support Vector Machines.

Computers, informatics, nursing : CIN Aug 1, 2017
We constructed a model using a support vector machine to determine whether an inpatient will suffer a fall on a given day, depending on patient status on the previous day. Using fall report data from our own facility and intensity-of-nursing-care-nee...
Nurse's Role Risk Assessment Hospitals Support Vector Machine Inpatients Accidental Falls Electronic Health Records Female Male Humans
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Leveraging Event Reporting Through Knowledge Support: A Knowledge-Based Approach to Promoting Patient Fall Prevention.

Studies in health technology and informatics Jan 1, 2017
Patient falls are a common safety event type that impairs the healthcare quality. Strategies including solution tools and reporting systems for preventing patient falls have been developed and implemented in the U.S. However, the current strategies d...
Knowledge Bases Humans Accidental Falls
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Estimation of Injury by Falls for Risk Assessment of Robotic Care Devices.

Studies in health technology and informatics Jan 1, 2017
In order for manufacturers of robotic devices for nursing care to predict injury in accidents related to their products, risk assessments are carried out according to various standards e.g. ISO 12100. Probabilities of death, bone fracture and head in...
Japan Humans Accidental Falls Robotics Risk Assessment
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Principal component analysis can decrease neural networks performance for incipient falls detection: A preliminary study with hands and feet accelerations.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
Fall-related accidents constitute a major problem for elderly people and a burden to the health-care national system. It is therefore important to design devices (e.g., accelerometers) and machine learning algorithms able to recognize incipient falls...
Monitoring, Ambulatory Accidental Falls Healthy Volunteers Machine Learning Principal Component Analysis Walking Humans Algorithms Acceleration Neural Networks, Computer Adult
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Relationship between magnitude of applied torque in pre-swing phase and gait change for prevention of trip in elderly people.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
Elderly people are at risk of tripping because of their narrow range of articular motion. To avoid tripping, gait training that improves their range of articular motion would be beneficial. In this study we propose a gait-training robot that applies ...
Biomechanical Phenomena Accidental Falls Knee Joint Walking Robotics Humans Aged Range of Motion, Articular Gait Disorders, Neurologic Gait Torque
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Can we make a carpet smart enough to detect falls?

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
In this paper, we have enhanced smart carpet, which is a floor based personnel detector system, to detect falls using a faster but low cost processor. Our hardware front end reads 128 sensors, with sensors output a voltage due to a person walking or ...
Floors and Floorcoverings Artificial Intelligence Monitoring, Ambulatory False Negative Reactions Algorithms Accidental Falls Databases, Factual Computers Humans Walking Video Recording Sensitivity and Specificity
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