Name
Abstract | ||
|---|---|---|
Acknowledging the powerful sensors on wearable mobile devices enabling various applications to improve users' life styles and qualities, this paper takes one step forward developing a automatic personal fitness assistance through wearable mobile devices to assess dynamic postures in workouts. In particular, our system recognizes different types of exercises and interprets fine-grained fitness data to an easy-to-understand exercise review score. The system has the ability to align the sensor readings from wearable devices to the earth coordinate system, ensuring the accuracy and robustness of the system. Experiments with 12 types of exercises involve multiple participants doing both anaerobic and aerobic exercises in indoors as well as outdoors. Our results demonstrate that the proposed system can provide meaningful review and recommendations to users by accurately measure their workout performance and achieve 93% accuracy for workout analysis. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2973750.2985266 | MobiCom |
Field | DocType | Citations |
Edge computing,Cloudlet,Computer science,Wearable computer,Robustness (computer science),Mobile device,Wearable technology,Multimedia | Conference | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaonan Guo | 1 | 8 | 5.27 |
| Jian Liu | 2 | 0 | 0.34 |
| Yingying Chen | 3 | 2495 | 193.14 |