AIMC Topic: Monitoring, Ambulatory
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Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms.
Heart (British Cardiac Society)
May 31, 2018
OBJECTIVE: To evaluate the diagnostic performance of a deep learning system for automated detection of atrial fibrillation (AF) in photoplethysmographic (PPG) pulse waveforms.
Human Activity Recognition from Body Sensor Data using Deep Learning.
Journal of medical systems
Apr 16, 2018
In recent years, human activity recognition from body sensor data or wearable sensor data has become a considerable research attention from academia and health industry. This research can be useful for various e-health applications such as monitoring...
Activities suggestion based on emotions in AAL environments.
Artificial intelligence in medicine
Feb 14, 2018
The elderly population is increasing and the response of the society was to provide them with services directed to them to cope with their needs. One of the oldest solutions is the retirement home, providing housing and permanent assistance for the e...
Quality of Life
Cognition
Age Factors
Emotions
Equipment Design
Humans
Artificial Intelligence
Aging
Boredom
Social Workers
Homes for the Aged
Delivery of Health Care, Integrated
Health Services for the Aged
Happiness
Activities of Daily Living
Monitoring, Ambulatory
Assisted Living Facilities
Self-Help Devices
Efficient Active Sensing with Categorized Further Explorations for a Home Behavior-Monitoring Robot.
Journal of healthcare engineering
Nov 28, 2017
Mobile robotics is a potential solution to home behavior monitoring for the elderly. For a mobile robot in the real world, there are several types of uncertainties for its perceptions, such as the ambiguity between a target object and the surrounding...
Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions.
Addictive behaviors
Nov 27, 2017
BACKGROUND: Real-time detection of drinking could improve timely delivery of interventions aimed at reducing alcohol consumption and alcohol-related injury, but existing detection methods are burdensome or impractical.
Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults.
PloS one
Nov 20, 2017
Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promis...
ROBIN, a Telepresence Robot to Support Older Users Monitoring and Social Inclusion: Development and Evaluation.
Telemedicine journal and e-health : the official journal of the American Telemedicine Association
Aug 3, 2017
INTRODUCTION: This article describes an enhanced telepresence robot named ROBIN, part of a telecare system derived from the GIRAFFPLUS project for supporting and monitoring older adults at home. ROBIN is integrated in a sensor-rich environment that a...
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...
Comparative Simulation Study of Glucose Control Methods Designed for Use in the Intensive Care Unit Setting via a Novel Controller Scoring Metric.
Journal of diabetes science and technology
Jun 22, 2017
BACKGROUND: Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates and thereby decrease health care expenditures. To evaluate what constitutes effective glucose control, typicall...
Activity Recognition for Persons With Stroke Using Mobile Phone Technology: Toward Improved Performance in a Home Setting.
Journal of medical Internet research
May 25, 2017
BACKGROUND: Smartphones contain sensors that measure movement-related data, making them promising tools for monitoring physical activity after a stroke. Activity recognition (AR) systems are typically trained on movement data from healthy individuals...
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