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Wireless body area network node localization using small-scale spatial information

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Title: Wireless body area network node localization using small-scale spatial information
Author: Lo, Geoffrey S.
Degree Master of Applied Science - MASc
Program Electrical and Computer Engineering
Copyright Date: 2012
Publicly Available in cIRcle 2012-09-21
Abstract: Deploying wireless body area networks (WBANs) in the long-term at-home monitoring of a patient’s physiological and bio-kinetic conditions has become increasingly prevalent. However, such WBANs do not typically incorporate mechanisms to detect and correct for the possibility of accidentally switching up wearable wireless sensor nodes (W²SNs), where a node assigned to one limb is placed on another, and vice-versa, leading to possible incorrect prognoses from interpreting the data. In this thesis, we present a new scheme to automatically identify and verify the locations of W²SNs in a WBAN. Using small-scale geospatial information, instantaneous atmospheric air pressures at each node are examined and compared to map and match them in physical space. By enhancing the context-awareness of WBANs, this enhancement enables unassisted sensor node placement, providing a practical solution to obtain and continuously monitor node locations at a sufficient resolution to recognize limb placement, without multidimensional fine-grain position information. Only a single atmospheric air pressure sensor (A²PS) is added to each W²SN; compared to existing localization techniques, no beacons or extra nodes are required, enabling an inexpensive and self-contained solution. To quantify and validate the accuracy, consistency and reliability of this localization scheme, a statistical analysis on a set of commercially-available air pressure sensors and an experimental prototype WBAN is conducted to examine the scheme’s performance and limitations. This study has verified that this approach is indeed capable of distinguishing between positions indicative of expected separation between different limbs of the patient’s body. Based on a 60cm separation between nodes, the statistical analysis consistently exceeded 95% accuracy within the confidence interval (CI), demonstrating great promise for incorporation into commercial WBANs. We also present and experimentally demonstrate an enhancement aiming to reduce false-positive (Type I) errors in conventional accelerometer-based on-body fall detection schemes. Our statistical analysis has shown that by continuously monitoring the patient’s limb positions, the W²SN position information would enable the WBAN to better classify ‘fall-like’ motion from actual falls, where the patient requires remote caregiver assistance.
URI: http://hdl.handle.net/2429/43256
Scholarly Level: Graduate

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