Name
Abstract | ||
|---|---|---|
Sensor-Based Human Activity Recognition (HAR) is a study of recognizing the human's activities by using the data captured from wearable sensors. Avail the temporal information from the sensors, a modified version of random forest is proposed to preserve the temporal information, and harness them in classifying the human activities. The proposed algorithm is tested on 7 public HAR datasets. Promising results are reported, with an average classification accuracy of similar to 98 %. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46681-1_1 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Human activity,Classification,Random forest,Temporal sequences,Machine learning | Activity recognition,Pattern recognition,Computer science,Wearable computer,Artificial intelligence,Random forest,Machine learning | Conference |
Volume | ISSN | Citations |
9950 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shih Yin Ooi | 1 | 23 | 6.80 |
| Shing Chiang Tan | 2 | 122 | 18.99 |
| Wooi Ping Cheah | 3 | 36 | 8.03 |