Name
Abstract | ||
|---|---|---|
We implemented a wired sensors system that supports activities identification. The system consists of Raspberry Pi, MPU6050 (accelerometers and gyrometers), and TCA9548 (1 to 8 multiplexer). Our experimental results show that when 6 MPU6050 attached to the right arm, right wrist, chest, waist, right thigh, and right ankle, the activities of standing, sitting, lying, walking, running, going upstairs, going downstairs, drinking water, and dumbbells activities could be identified with high accuracy. The system can connect up to 128 sensors, but under a practical sampling rate, the number of sensors should not be greater than 15. The system shall be used for finding the optimal locations for a multi-sensor wearable system (for examples, clothes or shoes).
|
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3123024.3123096 | UbiComp '17: The 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing
Maui
Hawaii
September, 2017 |
Keywords | Field | DocType |
Human Activity Recognition, Wearable sensors, physical activities | Computer science,Multiplexer,Artificial intelligence,Sitting,Computer vision,Right Thigh,Right wrist,Raspberry pi,Wearable computer,Simulation,Accelerometer,Right ankle,Embedded system | Conference |
ISBN | Citations | PageRank |
978-1-4503-5190-4 | 1 | 0.35 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Tai Ching | 1 | 2 | 2.41 |
| Chang-Chieh Cheng | 2 | 1 | 1.36 |
| Guan-Wei He | 3 | 2 | 0.71 |
| Yu-Jin Yang | 4 | 1 | 0.35 |