AIMC Topic: Smartphone

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A Fast and Robust Deep Convolutional Neural Networks for Complex Human Activity Recognition Using Smartphone.

Sensors (Basel, Switzerland) Aug 29, 2019
As a significant role in healthcare and sports applications, human activity recognition (HAR) techniques are capable of monitoring humans' daily behavior. It has spurred the demand for intelligent sensors and has been giving rise to the explosive gro...
Humans Neural Networks, Computer Algorithms Smartphone Human Activities Data Compression
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Recognizing basal cell carcinoma on smartphone-captured digital histopathology images with a deep neural network.

The British journal of dermatology Aug 22, 2019
BACKGROUND: Pioneering effort has been made to facilitate the recognition of pathology in malignancies based on whole-slide images (WSIs) through deep learning approaches. It remains unclear whether we can accurately detect and locate basal cell carc...
Carcinoma, Basal Cell Deep Learning Smartphone Humans Neural Networks, Computer Skin Neoplasms
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On-Device Deep Learning Inference for Efficient Activity Data Collection.

Sensors (Basel, Switzerland) Aug 5, 2019
Labeling activity data is a central part of the design and evaluation of human activity recognition systems. The performance of the systems greatly depends on the quantity and "quality" of annotations; therefore, it is inevitable to rely on users and...
Smartphone Human Activities Wireless Technology Humans Neural Networks, Computer Deep Learning
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Non-Invasive Tools to Detect Smoke Contamination in Grapevine Canopies, Berries and Wine: A Remote Sensing and Machine Learning Modeling Approach.

Sensors (Basel, Switzerland) Jul 30, 2019
Bushfires are becoming more frequent and intensive due to changing climate. Those that occur close to vineyards can cause smoke contamination of grapevines and grapes, which can affect wines, producing smoke-taint. At present, there are no available ...
Machine Learning Spectroscopy, Near-Infrared Smartphone Humans Fires Biosensing Techniques Fruit Wine Smoke Vitis
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Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches.

Schizophrenia research May 16, 2019
The ubiquity of smartphones opened up the possibility of widespread use of the Experience Sampling Method (ESM). The method is used to collect longitudinal data of participants' daily life experiences and is ideal to capture fluctuations in emotions ...
Emotions Principal Component Analysis Psychotic Disorders Smartphone Logistic Models Support Vector Machine Monte Carlo Method Ecological Momentary Assessment Neural Networks, Computer Machine Learning Female Male Case-Control Studies ROC Curve Humans
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Computer vision of smartphone video has potential to detect functional tremor.

Journal of the neurological sciences Apr 12, 2019
Tremor Essential Tremor Video Recording Accelerometry Humans Artificial Intelligence Smartphone
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Coarse-Fine Convolutional Deep-Learning Strategy for Human Activity Recognition.

Sensors (Basel, Switzerland) Mar 31, 2019
In the last decade, deep learning techniques have further improved human activity recognition (HAR) performance on several benchmark datasets. This paper presents a novel framework to classify and analyze human activities. A new convolutional neural ...
Middle Aged Sitting Position Deep Learning Walking Human Activities Humans Accelerometry Young Adult Smartphone Photography Adult
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Falls Risk Classification of Older Adults Using Deep Neural Networks and Transfer Learning.

IEEE journal of biomedical and health informatics Mar 27, 2019
Prior research in falls risk classification using inertial sensors has relied on the use of engineered features, which has resulted in a feature space containing hundreds of features that are likely redundant and possibly irrelevant. In this paper, w...
Adult Male Middle Aged Accidental Falls Humans Adolescent ROC Curve Aged Neural Networks, Computer Aged, 80 and over Smartphone Female Young Adult Signal Processing, Computer-Assisted Accelerometry
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Mass Surveilance of -Smartphone-Based DIY Microscope and Machine-Learning-Based Approach for Worm Detection.

Sensors (Basel, Switzerland) Mar 26, 2019
The nematode is often used as an alternative animal model due to several advantages such as morphological changes that can be seen directly under a microscope. Limitations of the model include the usage of expensive and cumbersome microscopes, and r...
Smartphone Microscopy Caenorhabditis elegans Machine Learning Animals Image Processing, Computer-Assisted
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Stress detection in daily life scenarios using smart phones and wearable sensors: A survey.

Journal of biomedical informatics Feb 27, 2019
Stress has become a significant cause for many diseases in the modern society. Recently, smartphones, smartwatches and smart wrist-bands have become an integral part of our lives and have reached a widespread usage. This raised the question of whethe...
Wearable Electronic Devices Female Humans Wrist Smartphone Male Machine Learning Activities of Daily Living Equipment Design Stress, Psychological Monitoring, Ambulatory
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