Abstract
Most of the studies on human activity recognition using smartphones and smartwatches are performed in an offline manner. In such studies, collected data is analyzed in machine learning tools with less focus on the resource consumption of these devices for running an activity recognition system. In this paper, we analyze the resource consumption of human activity recognition on both smartphones and smartwatches, considering six different classifiers, three
different sensors, different sampling rates and window sizes. We study the CPU, memory and battery usage with different parameters, where the smartphone is used to recognize seven physical activities and the smartwatch is used to recognize smoking activity. As a result of this analysis, we report that
classification function takes a very small amount of CPU time out of total app’s CPU time while sensing and feature calculation consume most of it. When an additional sensor is used besides an accelerometer, such as gyroscope, CPU
usage increases significantly. Analysis results also show that increasing the window size reduces the resource consumption more than reducing the sampling rate. As a final remark, we observe that a more complex model using only the accelerometer is a better option than using a simple model with both accelerometer and gyroscope when resource usage is to be reduced.
different sensors, different sampling rates and window sizes. We study the CPU, memory and battery usage with different parameters, where the smartphone is used to recognize seven physical activities and the smartwatch is used to recognize smoking activity. As a result of this analysis, we report that
classification function takes a very small amount of CPU time out of total app’s CPU time while sensing and feature calculation consume most of it. When an additional sensor is used besides an accelerometer, such as gyroscope, CPU
usage increases significantly. Analysis results also show that increasing the window size reduces the resource consumption more than reducing the sampling rate. As a final remark, we observe that a more complex model using only the accelerometer is a better option than using a simple model with both accelerometer and gyroscope when resource usage is to be reduced.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE 36th International Performance Computing and Communications Conference (IPCCC) |
| Place of Publication | Piscataway, NJ |
| Publisher | IEEE |
| ISBN (Electronic) | 978-1-5090-6468-7, 978-1-5090-6467-0 (USB) |
| ISBN (Print) | 978-1-5090-6469-4 |
| DOIs | |
| Publication status | Published - 11 Dec 2017 |
| Event | 36th IEEE International Performance Computing and Communications Conference, IPCCC 2017 - Bahia Resort Hotel, San Diego, United States Duration: 10 Dec 2017 → 12 Dec 2017 Conference number: 36 |
Conference
| Conference | 36th IEEE International Performance Computing and Communications Conference, IPCCC 2017 |
|---|---|
| Abbreviated title | IPCCC |
| Country/Territory | United States |
| City | San Diego |
| Period | 10/12/17 → 12/12/17 |
Keywords
- Mobile
- Performance analysis
- Activity recognition
- Sensors
- Smartwatch sensors