Name
Abstract | ||
|---|---|---|
Automatically recognizing people's daily activities is essential for a variety of applications, such as just-in-time content delivery or quantified self-tracking. Towards this, researchers often use customized wearable motion sensors tailored to recognize a small set of handpicked activities in controlled environments. In this paper, we design and engineer a scalable, daily activity recognition framework, by leveraging two widely adopted commercial devices: Android smartphone and Pebble smartwatch. Deploying our system outside the laboratory, we collected a total of more than 72 days of data from 12 user study participants. We systematically show the usefulness of time, location, and wrist-based motion for automatically recognizing 10 standardized activities, as specified by the American Time Use Survey taxonomy. Overall, we achieve a recognition accuracy of 76.28% for personalized models and 69.80% for generic, interpersonal models. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.4108/icst.bodynets.2014.257014 | BODYNETS |
Keywords | Field | DocType |
activity routine recognition,crowd-sensing platform,web repository exploitation,wearable sensors,learning | Android (operating system),Interpersonal communication,Activity recognition,Activities of daily living,Wearable computer,Computer science,American Time Use Survey,Human–computer interaction,Smartwatch,Multimedia,Scalability | Conference |
Citations | PageRank | References |
3 | 0.39 | 19 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zack Zhu | 1 | 67 | 5.52 |
| Ulf Blanke | 2 | 699 | 36.03 |
| Alberto Calatroni | 3 | 375 | 23.43 |
| Oliver Brdiczka | 4 | 3 | 0.73 |
| Gerhard Tröster | 5 | 2493 | 250.70 |