| Autor: |
Md Abdullah Al Hafiz Khan, H M Sajjad Hossain, Nirmalya Roy |
| Jazyk: |
angličtina |
| Rok vydání: |
2015 |
| Předmět: |
|
| Zdroj: |
EAI Endorsed Transactions on Context-aware Systems and Applications, Vol 2, Iss 5, Pp 1-10 (2015) |
| Druh dokumentu: |
article |
| ISSN: |
2409-0026 |
| DOI: |
10.4108/eai.22-7-2015.2260062 |
| Popis: |
Accurate estimation of localized occupancy related informa- tion in real time enables a broad range of intelligent smart environment applications. A large number of studies using heterogeneous sensor arrays reflect the myriad requirements of various emerging pervasive, ubiquitous and participatory sensing applications. In this paper, we introduce a zero- configuration and infrastructure-less smartphone based lo- cation specific occupancy estimation model. We opportunis- tically exploit smartphone’s acoustic sensors in a conversing environment and motion sensors in absence of any conver- sational data. We demonstrate a novel speaker estimation algorithm based on unsupervised clustering of overlapped and non-overlapped conversational data and a change point detection algorithm for locomotive motion of the users to infer the occupancy. We augment our occupancy detection model with a fingerprinting based methodology using smart- phone’s magnetometer sensor to accurately assimilate loca- tion information of any gathering. We postulate a novel crowdsourcing-based approach to annotate the semantic lo- cation of the occupancy. We evaluate our algorithms in dif- ferent contexts; conversational, silence and mixed in pres- ence of 10 domestic users. Our experimental results on real-life data traces in natural settings show that using this hybrid approach, we can achieve approximately 0.76 error count distance for occupancy detection accuracy on aver- age. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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