Name
Abstract | ||
|---|---|---|
This paper presents our recent work on human activity detection based on smart phone sensors and incremental clustering algorithms. The proposed unsupervised (clustering) activity detection scheme works in an incremental manner, which contains two stages. In the first stage, streamed sensor data will be processed. A single-pass clustering algorithm is used in order to generate pre-clustered results for the next stage. In the second stage, pre-clustered results will be refined to form the final clusters, which means the clusters are built incrementally adding one cluster at a time. Experiments on phone sensors data of five basic human activities show that the proposed scheme could get comparable results with traditional clustering algorithms but working in a streaming and incremental manner, which is promising for automatic annotated data collection. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CSCWD.2016.7565959 | 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD) |
Keywords | Field | DocType |
activity detection,sensors,incremental clustering,smart phones | Cluster (physics),Data mining,Data collection,Accelerometer,Computer science,Phone,Activity detection,Human activity detection,Smart phone,Cluster analysis | Conference |
ISBN | Citations | PageRank |
978-1-5090-1916-8 | 2 | 0.37 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xizhe Yin | 1 | 3 | 1.06 |
| Weiming Shen | 2 | 3407 | 343.73 |
| Xianbin Wang | 3 | 2365 | 223.86 |