@inProceedings{
 title = {Analysis of feature space for monitoring persons with Parkinson's disease with application to a wireless wearable sensor system},
 type = {inProceedings},
 year = {2007},
 identifiers = {[object Object]},
 keywords = {healthcare,mhealth,wireless},
 pages = {3686-3689},
 websites = {http://dx.doi.org/10.1109/IEMBS.2008.4650009},
 month = {8},
 publisher = {IEEE},
 id = {20b02d91-0123-323d-a41d-28dafa354f2a},
 created = {2018-07-12T21:31:02.558Z},
 file_attached = {false},
 profile_id = {f954d000-ce94-3da6-bd26-b983145a920f},
 group_id = {b0b145a3-980e-3ad7-a16f-c93918c606ed},
 last_modified = {2018-07-12T21:31:02.558Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {patel:parkinsons},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {We present work to develop a wireless wearable sensor system for monitoring patients with Parkinson's disease (PD) in their homes. For monitoring outside the laboratory, a wearable system must not only record data, but also efficiently process data on-board. This manuscript details the analysis of data collected using tethered wearable sensors. Optimal window length for feature extraction and feature ranking were calculated, based on their ability to capture motor fluctuations in persons with PD. Results from this study will be employed to develop a software platform for the wireless system, to efficiently process on-board data.},
 bibtype = {inProceedings},
 author = {Patel, Shyamal and Lorincz, Konrad and Hughes, Richard and Huggins, Nancy and Growdon, John H and Welsh, Matt and Bonato, Paolo},
 booktitle = {Proceedings of the IEEE Engineering in Medicine and Biology Society Conference (EMBS)}
}

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