AIMC Topic: Smartphone
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The development of an EU-wide nutrition and physical activity expert knowledge base to support a personalised mobile application across various EU population groups.
Nutrition bulletin
May 21, 2024
A healthy lifestyle comprising regular physical activity and an adequate diet is imperative for the prevention of non-communicable diseases such as hypertension and some cancers. Advances in information computer technology offer the opportunity to pr...
MocapMe: DeepLabCut-Enhanced Neural Network for Enhanced Markerless Stability in Sit-to-Stand Motion Capture.
Sensors (Basel, Switzerland)
May 10, 2024
This study examined the efficacy of an optimized DeepLabCut (DLC) model in motion capture, with a particular focus on the sit-to-stand (STS) movement, which is crucial for assessing the functional capacity in elderly and postoperative patients. This ...
Accurate fall risk classification in elderly using one gait cycle data and machine learning.
Clinical biomechanics (Bristol, Avon)
May 8, 2024
BACKGROUND: Falls among the elderly are a major societal problem. While observations of medium-distance walking using inertial sensors identified potential fall predictors, classifying individuals at risk based on single gait cycles remains elusive. ...
3D-printed portable device for illicit drug identification based on smartphone-imaging and artificial neural networks.
Talanta
May 6, 2024
In this manuscript, a 3D-printed analytical device has been successfully developed to classify illicit drugs using smartphone-based colorimetry. Representative compounds of different families, including cocaine, 3,4-methylenedioxy-methamphetamine (MD...
A Preliminary Evaluation of the Diagnostic Performance of a Smartphone-Based Machine Learning-Assisted System for Evaluation of Clinical Activity Score in Digital Images of Thyroid-Associated Orbitopathy.
Thyroid : official journal of the American Thyroid Association
May 2, 2024
We previously developed a machine learning (ML)-assisted system for predicting the clinical activity score (CAS) in thyroid-associated orbitopathy (TAO) using digital facial images taken by a digital single-lens reflex camera in a studio setting. In...
BlazePose-Seq2Seq: Leveraging Regular RGB Cameras for Robust Gait Assessment.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 1, 2024
Evaluation of human gait through smartphone-based pose estimation algorithms provides an attractive alternative to costly lab-bound instrumented assessment and offers a paradigm shift with real time gait capture for clinical assessment. Systems based...
Edge Artificial Intelligence (AI) for real-time automatic quantification of filariasis in mobile microscopy.
PLoS neglected tropical diseases
Apr 17, 2024
Filariasis, a neglected tropical disease caused by roundworms, is a significant public health concern in many tropical countries. Microscopic examination of blood samples can detect and differentiate parasite species, but it is time consuming and req...
Nucleic Acid Plate Culture: Label-Free and Naked-Eye-Based Digital Loop-Mediated Isothermal Amplification in Hydrogel with Machine Learning.
ACS sensors
Apr 11, 2024
Digital nucleic acid amplification enables the absolute quantification of single molecules. However, due to the ultrasmall reaction volume in the digital system (, short light path), most digital systems are limited to fluorescence signals, while lab...
Explainable prediction of problematic smartphone use among South Korea's children and adolescents using a Machine learning approach.
International journal of medical informatics
Mar 30, 2024
BACKGROUND: Korea is known for its technological prowess, has the highest smartphone ownership rate in the world at 95%, and the smallest gap in smartphone ownership between generations. Since the onset of the COVID-19 pandemic, problematic smartphon...
Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model.
Journal of affective disorders
Mar 27, 2024
BACKGROUND: Prior research has associated spoken language use with depression, yet studies often involve small or non-clinical samples and face challenges in the manual transcription of speech. This paper aimed to automatically identify depression-re...
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