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Smartphone

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Machine learning-based prediction of restless legs syndrome using digital phenotypes from wearables and smartphone data.

Scientific reports
Restless legs syndrome (RLS) is a relatively common neurosensory disorder that causes an irresistible urge for leg movement. RLS causes sleep disturbances and reduced quality of life, but accurate diagnosis remains challenging owing to the reliance o...

Machine learning of blood haemoglobin and haematocrit levels via smartphone conjunctiva photography in Kenyan pregnant women: a clinical study protocol.

BMJ open
INTRODUCTION: Anaemia during pregnancy is a widespread health burden globally, especially in low- and middle-income countries, posing a serious risk to both maternal and neonatal health. The primary challenge is that anaemia is frequently undetected ...

AI-Enabled Screening for Retinopathy of Prematurity in Low-Resource Settings.

JAMA network open
IMPORTANCE: Retinopathy of prematurity (ROP) is the leading cause of preventable childhood blindness worldwide. If detected and treated early, ROP-associated blindness is preventable; however, identifying patients who might respond to treatment requi...

Smartphone-Based SPAD Value Estimation for Jujube Leaves Using Machine Learning: A Study on RGB Feature Extraction and Hybrid Modeling.

Sensors (Basel, Switzerland)
Chlorophyll content in date leaves is critical for fruit quality and yield. Traditional detection methods are usually complex and expensive. This study proposes a rapid detection method for chlorophyll content using smartphone images and machine lear...

A Neural Network for Atrial Fibrillation Detection via PPG.

Studies in health technology and informatics
Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with severe complications such as ischemic stroke and heart failure. Early detection is essential for timely intervention; however, traditional diagnostic methods often lack scalab...

Predictors of smartphone addiction in adolescents with depression: combing the machine learning and moderated mediation model approach.

Behaviour research and therapy
Smartphone addiction (SA) significantly impacts the physical and mental health of adolescents, and can further exacerbate existing mental health issues in those with depression. However, fewer studies have focused on the predictors of SA in adolescen...

Design of Multi-Cancer VOCs Profiling Platform via a Deep Learning-Assisted Sensing Library Screening Strategy.

Analytical chemistry
The efficiency of sensor arrays in parallel discrimination of multianalytes is fundamentally influenced by the quantity and performance of the sensor elements. The advent of combinational design has notably accelerated the generation of chemical libr...

Deep-Learning-Assisted Microfluidic Immunoassay via Smartphone-Based Imaging Transcoding System for On-Site and Multiplexed Biosensing.

Nano letters
Point-of-care testing (POCT) with multiplexed capability, ultrahigh sensitivity, affordable smart devices, and user-friendly operation is critically needed for clinical diagnostics and food safety. This study presents a deep-learning-assisted microfl...

Advanced internet of things enhanced activity recognition for disability people using deep learning model with nature-inspired optimization algorithms.

Scientific reports
Human activity recognition has complex applications because of its worldly use of acquisition devices, namely video cameras and smartphones, and its capability to take human activity data. Human activity recognition became a hot scientific subject in...

Personalized prediction of negative affect in individuals with serious mental illness followed using long-term multimodal mobile phenotyping.

Translational psychiatry
Heightened negative affect is a core feature of serious mental illness. Over 90% of American adults own a smartphone, equipped with an array of sensors which can continuously and unobtrusively measure behaviors (e.g., activity levels, location, and p...