Name
Abstract | ||
|---|---|---|
Activity recognition systems have demonstrated potential in a broad range of applications. A crucial aspect of creating large scale human activity sensing corpus is to develop algorithms that perform activity recognition in a way that users are not limited to wear sensors on predefined locations on the body. Therefore, effective on-body sensor localization algorithms are needed to detect the location of wearable sensors automatically and in real-time. However, power optimization is a major concern in the design of these systems. Frequent need to charge multiple sensor nodes imposes much burden on the end-users. In this paper, we propose a novel signal processing approach that leverages feature selection algorithms to minimize power consumption of node localization. With the real data collected using wearable motion sensors, we demonstrate that the proposed approach achieves an energy saving that ranges from 88% to 99.59% while obtaining an accuracy performance between 73.15% and 99.85%. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2638728.2641313 | UbiComp Adjunct |
Keywords | Field | DocType |
health,human factors,human information processing,on-body sensor localization,body sensor networks,low power design,classification,machine learning,real-time and embedded systems | Signal processing,Data mining,Power optimization,Activity recognition,Feature selection,Computer science,Wearable computer,Real-time computing,Human–computer interaction,Motion sensors,Power consumption | Conference |
Citations | PageRank | References |
15 | 0.69 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ramyar Saeedi | 1 | 81 | 8.00 |
| Brian Schimert | 2 | 15 | 0.69 |
| Hassan Ghasemzadeh | 3 | 45 | 4.66 |