AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Smartphone

Showing 231 to 240 of 388 articles

Clear Filters

A Framework of Combining Short-Term Spatial/Frequency Feature Extraction and Long-Term IndRNN for Activity Recognition.

Sensors (Basel, Switzerland)
Smartphone-sensors-based human activity recognition is attracting increasing interest due to the popularization of smartphones. It is a difficult long-range temporal recognition problem, especially with large intraclass distances such as carrying sma...

Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning.

ACS nano
Deep-learning (DL)-based image processing has potential to revolutionize the use of smartphones in mobile health (mHealth) diagnostics of infectious diseases. However, the high variability in cellphone image data acquisition and the common need for l...

Using digital technologies in clinical trials: Current and future applications.

Contemporary clinical trials
In 2015, we provided an overview of the use of digital technologies in clinical trials, both as a methodological tool and as a mechanism to deliver interventions. At that time, there was limited guidance and limited use of digital technologies in cli...

Prediction of vascular aging based on smartphone acquired PPG signals.

Scientific reports
Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibilit...

A Review of Emotion Recognition Methods Based on Data Acquired via Smartphone Sensors.

Sensors (Basel, Switzerland)
In recent years, emotion recognition algorithms have achieved high efficiency, allowing the development of various affective and affect-aware applications. This advancement has taken place mainly in the environment of personal computers offering the ...

Smartphone Motion Sensor-Based Complex Human Activity Identification Using Deep Stacked Autoencoder Algorithm for Enhanced Smart Healthcare System.

Sensors (Basel, Switzerland)
Human motion analysis using a smartphone-embedded accelerometer sensor provided important context for the identification of static, dynamic, and complex sequence of activities. Research in smartphone-based motion analysis are implemented for tasks, s...

Deep Learning-Based Positioning of Visually Impaired People in Indoor Environments.

Sensors (Basel, Switzerland)
Wayfinding and navigation can present substantial challenges to visually impaired (VI) people. Some of the significant aspects of these challenges arise from the difficulty of knowing the location of a moving person with enough accuracy. Positioning ...

Predicting the first smoking lapse during a quit attempt: A machine learning approach.

Drug and alcohol dependence
BACKGROUND: Just-in-time adaptive interventions (JITAI) aim to prevent smoking lapse using tailored support delivered via mobile technology in the moments when it is most needed. Effective smoking cessation JITAI rely on the development of accurate d...

Artificial intelligence interventions focused on opioid use disorders: A review of the gray literature.

The American journal of drug and alcohol abuse
BACKGROUND: With the artificial intelligence (AI) paradigm shift comes momentum toward the development and scale-up of novel AI interventions to aid in opioid use disorder (OUD) care, in the identification of overdose risk, and in the reversal of ove...

Using Machine Learning and Smartphone and Smartwatch Data to Detect Emotional States and Transitions: Exploratory Study.

JMIR mHealth and uHealth
BACKGROUND: Emotional state in everyday life is an essential indicator of health and well-being. However, daily assessment of emotional states largely depends on active self-reports, which are often inconvenient and prone to incomplete information. A...