As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
Creation of a personalized adherence feedback loop is crucial for initiating and sustaining health behavior change. However, self reports are not sufficient to measure actual adherence. Recording and recognizing personal activities in a ubiquitous environment has thus emerged as a promising solution. In this work, we present a model-driven sensor data assessment mechanism capable of identifying high level adherence-related activity patterns from low level signals. The proposed intelligent sensing algorithm can learn from a population-based training data set and adapt quickly to an individual's exercise patterns using the acquired personal data. Upon the recognition of each activity, the system can further provide personalized feedback such as exercise coaching, fitness planning, and abnormal event detection. The resulted system demonstrates the feasibility of a portable real-time personalized adherence feedback system that could be used for advanced healthcare services.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.