Name
Abstract | ||
|---|---|---|
Sensors embedded in smart handheld can be extremely useful in providing information on peopleu0027s activities and behaviors, that can be extremely useful in smart home, smart healthcare applications. Existing work mostly uses one or more specific devices (with embedded sensors) for activity recognition and most of the time the detected activities are coarse grained like sit, walk etc. In this paper we propose a fine grained ubiquitous activity recognition system that uses only smartphone accelerometer that is available in any smartphone configuration. The system applies feature extraction and learning mechanism and selects minimal set of features that can precisely recognize twelve activities including static ( sit on chair/floor, lying left/straight/right) and dynamic ( slow/normal/brisk walk, running, climbing upstairs/downstairs, jogging, running) fine grained activities resulting in 92.7% accuracy with ensemble classifier. |
Year | Venue | Field |
|---|---|---|
2018 | ICDCN Workshops | Activity recognition,Computer science,Accelerometer,Real-time computing,Feature extraction,Home automation,Mobile device,Ubiquitous computing,Classifier (linguistics),Climbing,Distributed computing |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jayita Saha | 1 | 0 | 0.34 |
| Chandreyee Chowdhury | 2 | 30 | 12.18 |
| Ishan Roy Chowdhury | 3 | 0 | 0.34 |
| Priya Roy | 4 | 12 | 2.54 |