51 research outputs found
Wearable Fall Detector Using Recurrent Neural Networks
Falls have become a relevant public health issue due to their high prevalence and negative
effects in elderly people. Wearable fall detector devices allow the implementation of continuous
and ubiquitous monitoring systems. The effectiveness for analyzing temporal signals with low
energy consumption is one of the most relevant characteristics of these devices. Recurrent neural
networks (RNNs) have demonstrated a great accuracy in some problems that require analyzing
sequential inputs. However, getting appropriate response times in low power microcontrollers
remains a difficult task due to their limited hardware resources. This work shows a feasibility study
about using RNN-based deep learning models to detect both falls and falls’ risks in real time using
accelerometer signals. The effectiveness of four different architectures was analyzed using the SisFall
dataset at different frequencies. The resulting models were integrated into two different embedded
systems to analyze the execution times and changes in the model effectiveness. Finally, a study of
power consumption was carried out. A sensitivity of 88.2% and a specificity of 96.4% was obtained.
The simplest models reached inference times lower than 34 ms, which implies the capability to
detect fall events in real-time with high energy efficiency. This suggests that RNN models provide
an effective method that can be implemented in low power microcontrollers for the creation of
autonomous wearable fall detection systems in real-time
TPU Cloud-Based Generalized U-Net for Eye Fundus Image Segmentation
Medical images from different clinics are acquired with different instruments and settings.
To perform segmentation on these images as a cloud-based service we need to train with multiple datasets
to increase the segmentation independency from the source. We also require an ef cient and fast segmentation
network. In this work these two problems, which are essential for many practical medical imaging
applications, are studied. As a segmentation network, U-Net has been selected. U-Net is a class of deep
neural networks which have been shown to be effective for medical image segmentation. Many different
U-Net implementations have been proposed.With the recent development of tensor processing units (TPU),
the execution times of these algorithms can be drastically reduced. This makes them attractive for cloud
services. In this paper, we study, using Google's publicly available colab environment, a generalized fully
con gurable Keras U-Net implementation which uses Google TPU processors for training and prediction.
As our application problem, we use the segmentation of Optic Disc and Cup, which can be applied to
glaucoma detection. To obtain networks with a good performance, independently of the image acquisition
source, we combine multiple publicly available datasets (RIM-One V3, DRISHTI and DRIONS). As a result
of this study, we have developed a set of functions that allow the implementation of generalized U-Nets
adapted to TPU execution and are suitable for cloud-based service implementation.Ministerio de Economía y Competitividad TEC2016-77785-
Deep Learning System for COVID-19 Diagnosis Aid Using X-ray Pulmonary Images
The spread of the SARS-CoV-2 virus has made the COVID-19 disease a worldwide epidemic.
The most common tests to identify COVID-19 are invasive, time consuming and limited in resources.
Imaging is a non-invasive technique to identify if individuals have symptoms of disease in their
lungs. However, the diagnosis by this method needs to be made by a specialist doctor, which limits
the mass diagnosis of the population. Image processing tools to support diagnosis reduce the load by
ruling out negative cases. Advanced artificial intelligence techniques such as Deep Learning have
shown high effectiveness in identifying patterns such as those that can be found in diseased tissue.
This study analyzes the effectiveness of a VGG16-based Deep Learning model for the identification
of pneumonia and COVID-19 using torso radiographs. Results show a high sensitivity in the
identification of COVID-19, around 100%, and with a high degree of specificity, which indicates that
it can be used as a screening test. AUCs on ROC curves are greater than 0.9 for all classes considered
Low-Power Embedded System for Gait Classification Using Neural Networks
Abnormal foot postures can be measured during the march by plantar pressures in both
dynamic and static conditions. These detections may prevent possible injuries to the lower limbs like
fractures, ankle sprain or plantar fasciitis. This information can be obtained by an embedded instrumented
insole with pressure sensors and a low-power microcontroller. However, these sensors are placed in
sparse locations inside the insole, so it is not easy to correlate manually its values with the gait type; that is
why a machine learning system is needed. In this work, we analyse the feasibility of integrating a machine
learning classifier inside a low-power embedded system in order to obtain information from the user’s
gait in real-time and prevent future injuries. Moreover, we analyse the execution times, the power
consumption and the model effectiveness. The machine learning classifier is trained using an acquired
dataset of 3000+ steps from 6 different users. Results prove that this system provides an accuracy over
99% and the power consumption tests obtains a battery autonomy over 25 days
Analyzing recommender systems for health promotion using a multidisciplinary taxonomy: A scoping review
Background: Recommender systems are information retrieval systems that provide users with relevant items
(e.g., through messages). Despite their extensive use in the e-commerce and leisure domains, their application in
healthcare is still in its infancy. These systems may be used to create tailored health interventions, thus reducing
the cost of healthcare and fostering a healthier lifestyle in the population.
Objective: This paper identifies, categorizes, and analyzes the existing knowledge in terms of the literature
published over the past 10 years on the use of health recommender systems for patient interventions. The aim of
this study is to understand the scientific evidence generated about health recommender systems, to identify any
gaps in this field to achieve the United Nations Sustainable Development Goal 3 (SDG3) (namely, “Ensure healthy
lives and promote well-being for all at all ages”), and to suggest possible reasons for these gaps as well as to
propose some solutions.
Methods: We conducted a scoping review, which consisted of a keyword search of the literature related to health
recommender systems for patients in the following databases: ScienceDirect, PsycInfo, Association for Computing
Machinery, IEEExplore, and Pubmed. Further, we limited our search to consider only English-lan-guage journal
articles published in the last 10 years. The reviewing process comprised three researchers who filtered the results
simultaneously. The quantitative synthesis was conducted in parallel by two researchers, who classified each
paper in terms of four aspects—the domain, the methodological and procedural aspects, the health promotion
theoretical factors and behavior change theories, and the technical aspects—using a new multidisciplinary
taxonomy.
Results: Nineteen papers met the inclusion criteria and were included in the data analysis, for which thirty-three
features were assessed. The nine features associated with the health promotion theoretical factors and behavior
change theories were not observed in any of the selected studies, did not use principles of tailoring, and did not
assess (cost)-effectiveness.
Discussion: Health recommender systems may be further improved by using relevant behavior change strategies
and by implementing essential characteristics of tailored interventions. In addition, many of the features required
to assess each of the domain aspects, the methodological and procedural aspects, and technical aspects
were not reported in the studies.
Conclusions: The studies analyzed presented few evidence in support of the positive effects of using health recommender
systems in terms of cost-effectiveness and patient health outcomes. This is why future studies should
ensure that all the proposed features are covered in our multidisciplinary taxonomy, including integration with
electronic health records and the incorporation of health promotion theoretical factors and behavior change
theories. This will render those studies more useful for policymakers since they will cover all aspects needed to
determine their impact toward meeting SDG3.European Union's Horizon 2020 No 68112
Smart Footwear Insole for Recognition of Foot Pronation and Supination Using Neural Networks
Abnormal foot postures during gait are common sources of pain and pathologies of the
lower limbs. Measurements of foot plantar pressures in both dynamic and static conditions can detect
these abnormal foot postures and prevent possible pathologies. In this work, a plantar pressure
measurement system is developed to identify areas with higher or lower pressure load. This system
is composed of an embedded system placed in the insole and a user application. The instrumented
insole consists of a low-power microcontroller, seven pressure sensors and a low-energy bluetooth
module. The user application receives and shows the insole pressure information in real-time and,
finally, provides information about the foot posture. In order to identify the different pressure states
and obtain the final information of the study with greater accuracy, a Deep Learning neural network
system has been integrated into the user application. The neural network can be trained using a
stored dataset in order to obtain the classification results in real-time. Results prove that this system
provides an accuracy over 90% using a training dataset of 3000+ steps from 6 different users.Ministerio de Economía y Competitividad TEC2016-77785-
An Automated Fall Detection System Using Recurrent Neural Networks
Falls are the most common cause of fatal injuries in elderly
people, causing even death if there is no immediate assistance. Fall detection
systems can be used to alert and request help when this type of accident
happens. Certain types of these systems include wearable devices
that analyze bio-medical signals from the person carrying it in real time.
In this way, Deep Learning algorithms could automate and improve the
detection of unintentional falls by analyzing these signals. These algorithms
have proven to achieve high effectiveness with competitive performances
in many classification problems. This work aims to study 16
Recurrent Neural Networks architectures (using Long Short-Term Memory
and Gated Recurrent Units) for falls detection based on accelerometer
data, reducing computational requirements of previous research. The
architectures have been tested on a labeled version of the publicly available
SisFall dataset, achieving a mean F1-score above 0.73 and improving
state-of-the-art solutions in terms of network complexity.Ministerio de Economía y Competitivida TEC2016-77785-
Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks
Breast cancer is one of the most frequent causes of mortality in women. For the early detection of breast cancer,
the mammography is used as the most efficient technique to identify abnormalities such as tumors. Automatic
detection of tumors in mammograms has become a big challenge and can play a crucial role to assist doctors
in order to achieve an accurate diagnosis. State-of-the-art Deep Learning algorithms such as Faster Regional
Convolutional Neural Networks are able to determine the presence of an object and also its position inside
the image in a reduced computation time. In this work, we evaluate these algorithms to detect tumors in
mammogram images and propose a detection system that contains: (1) a preprocessing step performed on
mammograms taken from the Digital Database for Screening Mammography (DDSM) and (2) the Neural
Network model, which performs feature extraction over the mammograms in order to locate tumors within
each image and classify them as malignant or benign. The results obtained show that the proposed algorithm
has an accuracy of 97.375%. These results show that the system could be very useful for aiding physicians
when detecting tumors from mammogram images.Ministerio de Economía y Competitividad TEC2016-77785-
Multi-dataset Training for Medical Image Segmentation as a Service
Deep Learning tools are widely used for medical image segmentation. The results produced by these techniques depend to a great extent on the data sets used to train the used network. Nowadays many cloud service providers offer the required resources to train networks and deploy deep learning networks. This makes the idea of segmentation as a cloud-based service attractive. In this paper we study the possibility of training, a generalized configurable, Keras U-Net to test the feasibility of training with images acquired, with specific instruments, to perform predictions on data from other instruments. We use, as our application example, the segmentation of Optic Disc and Cup which can be applied to glaucoma detection. We use two publicly available data sets (RIM-One V3 and DRISHTI) to train either independently or combining their data.Ministerio de Economía y Competitividad TEC2016-77785-
Does Two-Class Training Extract Real Features? A COVID-19 Case Study
Diagnosis aid systems that use image analysis are currently very useful due to the large
workload of health professionals involved in making diagnoses. In recent years, Convolutional
Neural Networks (CNNs) have been used to help in these tasks. For this reason, multiple studies
that analyze the detection precision for several diseases have been developed. However, many of
these works distinguish between only two classes: healthy and with a specific disease. Based on
this premise, in this work, we try to answer the questions: When training an image classification
system with only two classes (healthy and sick), does this system extract the specific features of this
disease, or does it only obtain the features that differentiate it from a healthy patient? Trying to
answer these questions, we analyze the particular case of COVID-19 detection. Many works that
classify this disease using X-ray images have been published; some of them use two classes (with
and without COVID-19), while others include more classes (pneumonia, SARS, influenza, etc.). In
this work, we carry out several classification studies with two classes, using test images that do not
belong to those classes, in order to try to answer the previous questions. The first studies indicate
problems in these two-class systems when using a third class as a test, being classified inconsistently.
Deeper studies show that deep learning systems trained with two classes do not correctly extract the
characteristics of pathologies, but rather differentiate the classes based on the physical characteristics
of the images. After the discussion, we conclude that these two-class trained deep learning systems
are not valid if there are other diseases that cause similar symptoms.Junta de Andalucía and FEDER research project MSF-PHIA (US-1263715
- …