AIMC Topic: Humans

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Spatio-Temporal Abnormal Behavior Prediction in Elderly Persons Using Deep Learning Models.

Sensors (Basel, Switzerland)
The ability to identify and accurately predict abnormal behavior is important for health monitoring systems in smart environments. Specifically, for elderly persons wishing to maintain their independence and comfort in their living spaces, abnormal b...

Deep CNN Sparse Coding for Real Time Inhaler Sounds Classification.

Sensors (Basel, Switzerland)
Effective management of chronic constrictive pulmonary conditions lies in proper and timely administration of medication. As a series of studies indicates, medication adherence can effectively be monitored by successfully identifying actions performe...

Association Rule Learning Is an Easy and Efficient Method for Identifying Profiles of Traumas and Stressors that Predict Psychopathology in Disaster Survivors: The Example of Sri Lanka.

International journal of environmental research and public health
Research indicates that psychopathology in disaster survivors is a function of both experienced trauma and stressful life events. However, such studies are of limited utility to practitioners who are about to go into a new post-disaster setting as (1...

Contactless Real-Time Heartbeat Detection via 24 GHz Continuous-Wave Doppler Radar Using Artificial Neural Networks.

Sensors (Basel, Switzerland)
The measurement of human vital signs is a highly important task in a variety of environments and applications. Most notably, the electrocardiogram (ECG) is a versatile signal that could indicate various physical and psychological conditions, from sig...

Application of a convolutional neural network for predicting the occurrence of ventricular tachyarrhythmia using heart rate variability features.

Scientific reports
Predicting the occurrence of ventricular tachyarrhythmia (VTA) in advance is a matter of utmost importance for saving the lives of cardiac arrhythmia patients. Machine learning algorithms have been used to predict the occurrence of imminent VTA. In t...

Key components of mechanical work predict outcomes in robotic stroke therapy.

Journal of neuroengineering and rehabilitation
BACKGROUND: Clinical practice typically emphasizes active involvement during therapy. However, traditional approaches can offer only general guidance on the form of involvement that would be most helpful to recovery. Beyond assisting movement, robots...

Machine Learning Algorithms in Suicide Prevention: Clinician Interpretations as Barriers to Implementation.

The Journal of clinical psychiatry
OBJECTIVE: Machine learning algorithms in electronic medical records can classify patients by suicide risk, but no research has explored clinicians' perceptions of suicide risk flags generated by these algorithms, which may affect algorithm implement...

Population coupling predicts the plasticity of stimulus responses in cortical circuits.

eLife
Some neurons have stimulus responses that are stable over days, whereas other neurons have highly plastic stimulus responses. Using a recurrent network model, we explore whether this could be due to an underlying diversity in their synaptic plasticit...

Exploring the Role of Artificial Intelligence in an Emergency and Trauma Radiology Department.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Emergency and trauma radiologists, emergency department's physicians and nurses, researchers, departmental leaders, and health policymakers have attempted to discover efficient approaches to enhance the provision of quality patient care. There are in...

Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT.

European journal of nuclear medicine and molecular imaging
PURPOSE: In selective internal radiation therapy (SIRT), an accurate total liver segmentation is required for activity prescription and absorbed dose calculation. Our goal was to investigate the feasibility of using automatic liver segmentation based...