Name
Abstract | ||
|---|---|---|
A variety of self-tracking applications and devices have been developed in recent years to support users in tracking their weight, calories eaten, physical activities, sleep and productivity. The availability of all this data from multiple streams provides a rich environment for experimentation that allows users to improve certain aspects of their lives such as losing weight, getting better sleep or being more productive. In this paper we propose a framework that guides users to define, track, analyse, improve and control goals for better personal productivity. We present the outcome of a single-subject case study that was implemented over one year based on the proposed framework for academic productivity. This pilot study demonstrates how longitudinal multistream self-tracking data can be leveraged to gain actionable insights into personal productivity. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CBMI.2019.8877475 | 2019 International Conference on Content-Based Multimedia Indexing (CBMI) |
Keywords | Field | DocType |
physical activities,multiple streams,rich environment,single-subject case study,academic productivity,longitudinal multistream self-tracking data,personal productivity enhancement,quantified-self framework | Data science,Computer vision,Computer science,Software,Artificial intelligence | Conference |
ISSN | ISBN | Citations |
1949-3983 | 978-1-7281-4674-4 | 0 |
PageRank | References | Authors |
0.34 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gary White | 1 | 0 | 0.34 |
| Zilu Liang | 2 | 0 | 0.34 |
| Siobhán Clarke | 3 | 699 | 87.36 |