Experimental analysis of a mobile health system for mood disorders.
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Date
2010-07-27
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Abstract
Depression is one of the leading causes of disability Methods are needed to quantitatively classify emotions in order to better understand and treat mood disorders This research proposes techniques to improve communication in body sensor network BSN that gathers data on the affective states of the patient These BSNs can continuously monitor discretely quantify and classify a patient s depressive states In addition data on the patient s lifestyle can be correlated with his her physiological conditions to identify how various stimuli trigger symptoms This continuous stream of data is an improvement over a snapshot of localized symptoms that a doctor often collects during a medical examination Our research first quantifies how the body interferes with communication in a BSN and detects a pattern between the line of sight of an embedded device and its reception rate Then a mathematical model of the data using linear programming techniques determines the optimal placement and number of sensors in a BSN to improve communication Experimental results show that the optimal placement of embedded devices can reduce power cost up to 27 and reduce hardware costs up to 47 This research brings researchers a step closer to continuous real time systemic monitoring that will allow one to analyze the dynamic human physiology and understand diagnosis and treat mood disorders