Name
Abstract | ||
|---|---|---|
This paper describes an orientation-independent method for detecting activities of daily living based on reference coordinate transformation. With the proposed method, a classification model can be trained using data acquired during a specific sensor orientation and applied to other input signals regardless of the orientation of the device. The technique is validated using activity recognition experiments with four different orientations of a single tri-axial accelerometer placed on the waist of 13 subjects performing a sub-class of activities of daily living. A high subject-independent accuracy of 90.42% has been achieved, reflecting a significant improvement of 11.74% and 16.58%, compared with classification without input transformation and classification with orientation-specific models, respectively. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/BSN.2010.55 | BSN |
Keywords | Field | DocType |
orientation-independent method,input transformation,classification model,orientation-specific model,high subject-independent accuracy,specific sensor orientation,activity recognition experiment,different orientation,device-orientation independent method,activity recognition,daily living,accuracy,acceleration,oxygen,activity of daily living,intelligent sensors,coordinate transformation,daily living activities,wearable computers,biomechanics,accelerometers,feature extraction | Coordinate system,Computer vision,Activity recognition,Computer science,Intelligent sensor,Accelerometer,Feature extraction,Artificial intelligence,Signal classification,Motion analysis,Context sensing | Conference |
Citations | PageRank | References |
26 | 1.97 | 7 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Surapa Thiemjarus | 1 | 192 | 15.64 |