Name
Title | ||
|---|---|---|
Human Activity Detection Based On Multiple Smart Phone Sensors And Machine Learning Algorithms |
Abstract | ||
|---|---|---|
This paper presents our recent work on human activity detection based on smart phone embedded sensors and learning algorithms. The proposed human activity detection system recognizes human activities including walking, running, and sitting. While walking and running can be recorded as daily fitness activities, falling will also be detected as anomalous situations and alerting messages can be sent as needed. Embedded sensors including a tri-axial accelerometer, tri-axial linear accelerometer, gyroscope sensor, and orientation sensors are used for motion data collection. A two-stage data analysis approach is used for prediction model generation: short period statistical analysis (max, min, mean, and standard deviation) and long period data analysis using machine learning. The system is implemented in an Android smart phone platform. |
Year | Venue | Keywords |
|---|---|---|
2015 | PROCEEDINGS OF THE 2015 IEEE 19TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN (CSCWD) | activity detection, sensors, machine learning, smart phones |
Field | DocType | Citations |
Data collection,Gyroscope,Android (operating system),Accelerometer,Computer science,Support vector machine,Algorithm,Artificial intelligence,Human activity detection,Smart phone,Standard deviation,Machine learning | Conference | 1 |
PageRank | References | Authors |
0.36 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xizhe Yin | 1 | 3 | 1.06 |
| Weiming Shen | 2 | 3407 | 343.73 |
| Jagath Samarabandu | 3 | 133 | 20.50 |
| Xianbin Wang | 4 | 2365 | 223.86 |