Towards Mining Frequent Patterns in Genome Wide Association

Mutalib, Sofianita; Abdul-Rahman, Shuzlina; Mohamed, Azlinah

doi:10.1109/cse.2013.159

Cited by 2 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A study by [15], focused on to test people who are not under medical treatment but may have risk factors that can lead to chronic illness with pre-existing medical conditions. The main goal of the study is to increase their level of physical activity in their daily lives in order to meet the standard recommendations of health [4]. This study distinguishes four specific context categories which are time, location, activity and identity.…”

Section: Trans-theoreticalmentioning

confidence: 99%

“…In order to generate precise recommendations for physical activity, the guidelines from WHO are used [4].…”

Section: Proposed Frameworkmentioning

confidence: 99%

“…Some wearable devices and applications not only tracking fitness data but the applications also able to recommend a general physical activity to stay healthy. For example, adult aged 18-64 should do at least 150 minutes per week of moderate intensity aerobic physical activity in order to reduce the risk of chronic disease and depression [4]. Several initiatives have been explored in recent years to encourage physical activity with wearable technologies, particularly smartphones [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Physical activity prediction using fitness data: Challenges and issues

Zakariya

Rosli

2021

Bulletin EEI

View full text Add to dashboard Cite

In the new healthcare transformations, individuals are encourage to maintain healthy life based on their food diet and physical activity routine to avoid risk of serious disease. One of the recent healthcare technologies to support self health monitoring is wearable device that allow individual play active role on their own healthcare. However, there is still questions in terms of the accuracy of wearable data for recommending physical activity due to enormous fitness data generated by wearable devices. In this study, we conducted a literature review on machine learning techniques to predict suitable physical activities based on personal context and fitness data. We categorize and structure the research evidence that has been publish in the area of machine learning techniques for predicting physical activities using fitness data. We found 10 different models in Behavior Change Technique (BCT) and we selected two suitable models which are Fogg Behavior Model (FBM) and Trans-theoretical Behavior Model (TTM) for predicting physical activity using fitness data. We proposed a conceptual framework which consists of personal fitness data, combination of TTM and FBM to predict the suitable physical activity based on personal context. This study will provide new insights in software development of healthcare technologies to support personalization of individuals in managing their own health.

show abstract

Section: Trans-theoreticalmentioning

confidence: 99%

“…In order to generate precise recommendations for physical activity, the guidelines from WHO are used [4].…”

Section: Proposed Frameworkmentioning

confidence: 99%