95,447 research outputs found
A Data Science and Machine Learning Approach to Measure and Monitor Physical Activity in Children
Physical Activity is a fundamental component for the maintenance of a healthy lifestyle. Recommendations for physical activity levels are issued by most governments as part of public health measures. Therefore, it is vital for regulatory purposes, that there are reliable measurements of physical activity. However, the techniques and protocols used in existing physical activity research, including laboratory-based measurement, have received increasingly critical scrutiny in recent times. Consequently, physical activity researchers have begun to explore the use of wearable sensing technology to capture large amounts of data and the use of machine learning techniques, specifically artificial neural networks, to produce classifications for specific physical activity events. This paper explores this idea further and presents a supervised machine learning approach that utilises data obtained from accelerometer sensors worn by children in free-living environments. The paper posits a rigorous data science approach that presents a set of activities and features suitable for measuring physical activity in children in free-living environments. A Multilayer Perceptron neural network is used to classify physical activities by activity type, using ecologically valid data from body worn accelerometer sensors. A rigorous reproducible data science methodology is presented for subsequent use in physical activity research. Our results show that it was possible to obtain an overall accuracy of 92% using the initial data set, and 99.8% using interpolated cases
A Machine Learning Approach to Measure and Monitor Physical Activity in Children to Help Fight Overweight and Obesity
Physical Activity is important for maintaining healthy lifestyles. Recommendations for physical activity levels are issued by most governments as part of public health measures. As such, reliable measurement of physical activity for regulatory purposes is vital. This has lead research to explore standards for achieving this using wearable technology and artificial neural networks that produce classifications for specific physical activity events. Applied from a very early age, the ubiquitous capture of physical activity data using mobile and wearable technology may help us to understand how we can combat childhood obesity and the impact that this has in later life. A supervised machine learning approach is adopted in this paper that utilizes data obtained from accelerometer sensors worn by children in free-living environments. The paper presents a set of activities and features suitable for measuring physical activity and evaluates the use of a Multilayer Perceptron neural network to classify physical activities by activity type. A rigorous reproducible data science methodology is presented for subsequent use in physical activity research. Our results show that it was possible to obtain an overall accuracy of 96 % with 95 % for sensitivity, 99 % for specificity and a kappa value of 94 % when three and four feature combinations were used
BigO: A public health decision support system for measuring obesogenic behaviors of children in relation to their local environment
Obesity is a complex disease and its prevalence depends on multiple factors
related to the local socioeconomic, cultural and urban context of individuals.
Many obesity prevention strategies and policies, however, are horizontal
measures that do not depend on context-specific evidence. In this paper we
present an overview of BigO (http://bigoprogram.eu), a system designed to
collect objective behavioral data from children and adolescent populations as
well as their environment in order to support public health authorities in
formulating effective, context-specific policies and interventions addressing
childhood obesity. We present an overview of the data acquisition, indicator
extraction, data exploration and analysis components of the BigO system, as
well as an account of its preliminary pilot application in 33 schools and 2
clinics in four European countries, involving over 4,200 participants.Comment: Accepted version to be published in 2020, 42nd Annual International
Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),
Montreal, Canad
“Stickiness”: Gauging students’ attention to online learning activities
Purpose: Online content developers use the term “stickiness” to refer to the ability of their online service or game to attract and hold the attention of users and create a compelling and magnetic reason for them to return repeatedly (examples include virtual pets and social media). In business circles, the same term connotes the level of consumer loyalty to a particular brand. This paper aims to extend the concept of “stickiness” not only to describe repeat return and commitment to the learning “product”, but also as a measure of the extent to which students are engaged in online learning opportunities.
Design/methodology/approach: This paper explores the efficacy of several approaches to the monitoring and measuring of online learning environments, and proposes a framework for assessing the extent to which these environments are compelling, engaging and “sticky”.
Findings: In particular, the exploration so far has highlighted the difference between how lecturers have monitored the engagement of students in a face-to-face setting versus the online teaching environment.
Practical implications: In the higher education environment where increasingly students are being asked to access learning in the online space, it is vital for teachers to be in a position to monitor and guide students in their engagement with online materials.
Originality/value: The mere presence of learning materials online is not sufficient evidence of engagement. This paper offers options for testing specific attention to online materials allowing greater assurance around engagement with relevant and effective online learning activities
How will the Internet of Things enable Augmented Personalized Health?
Internet-of-Things (IoT) is profoundly redefining the way we create, consume,
and share information. Health aficionados and citizens are increasingly using
IoT technologies to track their sleep, food intake, activity, vital body
signals, and other physiological observations. This is complemented by IoT
systems that continuously collect health-related data from the environment and
inside the living quarters. Together, these have created an opportunity for a
new generation of healthcare solutions. However, interpreting data to
understand an individual's health is challenging. It is usually necessary to
look at that individual's clinical record and behavioral information, as well
as social and environmental information affecting that individual. Interpreting
how well a patient is doing also requires looking at his adherence to
respective health objectives, application of relevant clinical knowledge and
the desired outcomes.
We resort to the vision of Augmented Personalized Healthcare (APH) to exploit
the extensive variety of relevant data and medical knowledge using Artificial
Intelligence (AI) techniques to extend and enhance human health to presents
various stages of augmented health management strategies: self-monitoring,
self-appraisal, self-management, intervention, and disease progress tracking
and prediction. kHealth technology, a specific incarnation of APH, and its
application to Asthma and other diseases are used to provide illustrations and
discuss alternatives for technology-assisted health management. Several
prominent efforts involving IoT and patient-generated health data (PGHD) with
respect converting multimodal data into actionable information (big data to
smart data) are also identified. Roles of three components in an evidence-based
semantic perception approach- Contextualization, Abstraction, and
Personalization are discussed
Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia
Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials
Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder
The present methods of diagnosing depression are entirely dependent on self-report
ratings or clinical interviews. Those traditional methods are subjective, where the individual may
or may not be answering genuinely to questions. In this paper, the data has been collected using
self-report ratings and also using electronic smartwatches. This study aims to develop a weighted
average ensemble machine learning model to predict major depressive disorder (MDD) with superior
accuracy. The data has been pre-processed and the essential features have been selected using a
correlation-based feature selection method. With the selected features, machine learning approaches
such as Logistic Regression, Random Forest, and the proposedWeighted Average Ensemble Model are
applied. Further, for assessing the performance of the proposed model, the Area under the Receiver
Optimization Characteristic Curves has been used. The results demonstrate that the proposed
Weighted Average Ensemble model performs with better accuracy than the Logistic Regression and
the Random Forest approaches
Measures for assessing the impact of ICT use on attainment
"Building on ImpaCT2, this study aims to design a measure or measures capable of tracking 'snapshot' data, such that it will be possible to monitor the development of ICT use to support attainment" -- page 4
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