AIMC Topic: Exercise

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Applying machine learning to predict future adherence to physical activity programs.

BMC medical informatics and decision making
BACKGROUND: Identifying individuals who are unlikely to adhere to a physical exercise regime has potential to improve physical activity interventions. The aim of this paper is to develop and test adherence prediction models using objectively measured...

Using street view data and machine learning to assess how perception of neighborhood safety influences urban residents' mental health.

Health & place
Previous studies have shown that perceptions of neighborhood safety are associated with various mental health outcomes. However, scant attention has been paid to the mediating pathways by which perception of neighborhood safety affects mental health....

Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity.

Scientific reports
Human age estimation is an important and difficult challenge. Different biomarkers and numerous approaches have been studied for biological age estimation, each with its advantages and limitations. In this work, we investigate whether physical activi...

Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes.

Artificial intelligence in medicine
BACKGROUND: Diabetes mellitus (DM) is a metabolic disorder that causes abnormal blood glucose (BG) regulation that might result in short and long-term health complications and even death if not properly managed. Currently, there is no cure for diabet...

The linkage between the perception of neighbourhood and physical activity in Guangzhou, China: using street view imagery with deep learning techniques.

International journal of health geographics
BACKGROUND: Neighbourhood environment characteristics have been found to be associated with residents' willingness to conduct physical activity (PA). Traditional methods to assess perceived neighbourhood environment characteristics are often subjecti...

Phenotyping Women Based on Dietary Macronutrients, Physical Activity, and Body Weight Using Machine Learning Tools.

Nutrients
Nutritional phenotyping can help achieve personalized nutrition, and machine learning tools may offer novel means to achieve phenotyping. The primary aim of this study was to use energy balance components, namely input (dietary energy intake and macr...

Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks.

Sensors (Basel, Switzerland)
Photoplethysmography (PPG)-based continuous heart rate monitoring is essential in a number of domains, e.g., for healthcare or fitness applications. Recently, methods based on time-frequency spectra emerged to address the challenges of motion artefac...

Evaluating and Enhancing the Generalization Performance of Machine Learning Models for Physical Activity Intensity Prediction From Raw Acceleration Data.

IEEE journal of biomedical and health informatics
PURPOSE: To evaluate and enhance the generalization performance of machine learning physical activity intensity prediction models developed with raw acceleration data on populations monitored by different activity monitors.

Effects of obesity on breast size, thoracic spine structure and function, upper torso musculoskeletal pain and physical activity in women.

Journal of sport and health science
PURPOSE: This study investigated the effects of obesity on breast size, thoracic spine structure and function, upper torso musculoskeletal pain and physical activity participation in women living independently in the community.

Developing a Physical Activity Ontology to Support the Interoperability of Physical Activity Data.

Journal of medical Internet research
BACKGROUND: Physical activity data provides important information on disease onset, progression, and treatment outcomes. Although analyzing physical activity data in conjunction with other clinical and microbiological data will lead to new insights c...