Name
Abstract | ||
|---|---|---|
Activity recognition has become of great importance in many fields especially in fitness monitoring; health and elder care by offering the opportunity for large amount of applications which recognize human's daily life activities. The prevalence of smart phones in our society with their ever growing sensing power has opened the door for more sophisticated data mining applications which takes the raw sensor data as input and classify the motion activity performed. The main sensor used in performing activity recognition is the accelerometer. This paper presents a framework for activity recognition using smart phone sensors. Features extracted from raw sensor data are used to train and test supervised machine learning algorithms. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICCTA37466.2015.9513456 | 2015 25th International Conference on Computer Theory and Applications (ICCTA) |
Keywords | DocType | ISBN |
Activity recognition,mobile sensor,accelerometer,human activity,smart devices | Conference | 978-1-5090-0218-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Walid Mohamed Aly | 1 | 0 | 0.34 |
| Khaled Eskaf | 2 | 0 | 0.34 |
| Alyaa Aly | 3 | 0 | 0.34 |