Applying Data Mining Techniques to Identify Suitable Activities

Identifying suitable physical activities is crucial for personal health management. However, a big challenge in identifying suitable physical activities is the influencing factors are extremely complex. Therefore, this study aims to propose an approach to facilitate the construction of suitable physical activity models. In the approach, association rule mining and clustering technique are applied to analyze personal activity-physiological information. To demonstrate how the proposed approach can be used for constructing the activity models, an experiment using mobile devices to collect personal activity-physiological information was designed. The revealed models can be used to not only understand personal health conditions but also provide useful information about proper and improper physical activities