Wearable Analytics :a systematic survey and an evidence-based framework

Speaker:  Athena Vakali – Thessaloniki, Greece
Topic(s):  Information Systems, Search, Information Retrieval, Database Systems, Data Mining, Data Science


In today’s connected society, many people rely on mHealth and self-tracking (ST) technology to help them break their sedentary lifestyle and stay fit. However, there is scarce evidence of such technological interventions’ effectiveness, and there is no standardized method to evaluate their short- and long-term impact on people’s physical activity and health. This work aims to help ST practitioners and researchers by empowering them with systematic guidelines and an extensible framework for designing and evaluating such technological interventions. This survey and the proposed design and evaluation framework aim to contribute to health behavior change and user engagement sustainability. To this end, we conduct a literature review of 117 papers between 2008 and 2020, which identifies the core ST design techniques and their efficacy, as well as and the
most comprehensive list to date of user engagement evaluation metrics for ST. Based on the review’s findings, we propose the PAST SELF end-to-end framework to facilitate the classification, design, and evaluation of ST technology. The PAST SELF framework systematically organizes common methods and guidelines from existing works in ubiquitous ST research. Hence, it has potential applications in both industrial and scientific settings and can be utilized by practitioners and researchers alike.

About this Lecture

Number of Slides:  27-30
Duration:  50-60 minutes
Languages Available:  English, Greek
Last Updated: 

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