Name
Abstract | ||
|---|---|---|
Wireless body area sensing networks have the potential to revolutionize health care in the near term. The coupling of biosensors with a wireless infrastructure enables the real-time monitoring of an individual's health and related behaviors continuously, as well as the provision of realtime feedback with nimble, adaptive, and personalized interventions. The KNOWME platform is reviewed, and lessons learned from system integration, optimization, and in-field deployment are provided. KNOWME is an endto- end body area sensing system that integrates off-the-shelf sensors with a Nokia N95 mobile phone to continuously monitor and analyze the biometric signals of a subject. KNOWME development by an interdisciplinary team and in-laboratory, as well as in-field deployment studies, employing pediatric obesity as a case study condition to monitor and evaluate physical activity, have revealed four major challenges: (1) achieving robustness to highly varying operating environments due to subject-induced variability such as mobility or sensor placement, (2) balancing the tension between acquiring high fidelity data and minimizing network energy consumption, (3) enabling accurate physical activity detection using a modest number of sensors, and (4) designing WBANs to determine physiological quantities of interest such as energy expenditure. The KNOWME platform described in this article directly addresses these challenges. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/MCOM.2012.6194391 | Communications Magazine, IEEE |
Keywords | Field | DocType |
biosensors,body area networks,energy consumption,health care,mobility management (mobile radio),patient monitoring,sensor placement,KNOWME platform,Nokia N95 mobile phone,biometric signal,biosensor coupling,end-to-end body area sensing system,energy expenditure,health care,in-field deployment,mobility,network energy consumption minimisation,off-the-shelf sensor,pediatric obesity,physical activity detection,physical activity monitoring,physiological quantity,real-time health monitoring,realtime feedback,sensor placement,subject-induced variability,wireless body area sensor network design,wireless infrastructure | Software deployment,Wireless,Computer science,Remote patient monitoring,Computer network,Robustness (computer science),Mobile phone,Energy consumption,Wireless sensor network,Distributed computing,System integration | Journal |
Volume | Issue | ISSN |
50 | 5 | 0163-6804 |
Citations | PageRank | References |
32 | 1.15 | 6 |
Authors | ||
13 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Urbashi Mitra | 1 | 1336 | 229.37 |
| B Adar Emken | 2 | 87 | 6.70 |
| Sangwon Lee | 3 | 56 | 3.18 |
| Ming Li | 4 | 5595 | 829.00 |
| Viktor Rozgic | 5 | 82 | 9.22 |
| Gautam Thatte | 6 | 56 | 3.46 |
| Harshvardhan Vathsangam | 7 | 92 | 8.42 |
| Daphney-Stavroula Zois | 8 | 82 | 8.63 |
| Murali Annavaram | 9 | 1685 | 113.77 |
| Narayanan Shrikanth | 10 | 5558 | 439.23 |
| Marco Levorato | 11 | 464 | 49.51 |
| Donna Spruijt-Metz | 12 | 77 | 8.30 |
| Gaurav S. Sukhatme | 13 | 5469 | 548.13 |