000227938 001__ 227938
000227938 005__ 20190509132609.0
000227938 0247_ $$2doi$$a10.5075/epfl-thesis-7653
000227938 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis7653-7
000227938 02471 $$2nebis$$a10890335
000227938 037__ $$aTHESIS
000227938 041__ $$aeng
000227938 088__ $$a7653
000227938 245__ $$aComplex event recognition through wearable sensors
000227938 260__ $$bEPFL$$c2017$$aLausanne
000227938 269__ $$a2017
000227938 300__ $$a138
000227938 336__ $$aTheses
000227938 502__ $$aprofesseure Sabine Süsstrunk (présidente) ; Prof. Karl Aberer (directeur de thèse) ; Prof. Daniel Gatica-Perez, Prof. Michael Schumacher, Dr Sougata Mukherjea (rapporteurs)
000227938 520__ $$aComplex events are instrumental in understanding advanced behaviours and properties of a system. They can represent more meaningful events as compared to simple events. In this thesis we propose to use wearable sensor signals to detect complex events. These signals are pertaining to the user's state and therefore allow us to understand advanced characteristics about her. We propose a hierarchical approach to detect simple events from the wearable sensors data and then build complex events on top of them.  In order to address privacy concerns that rise from the use of sensitive signals, we propose to perform all the computation on device. While this ensures the privacy of the data, it poses the problem of having limited computational resources. This problem is tackled by introducing energy efficient approaches based on incremental algorithms.  A second challenge is the multiple levels of noise in the process. A first level of noise concerns the raw signals that are inherently imprecise (e.g. inaccuracy in GPS readings). A second level of noise, that we call semantic noise, is present among the simple events detected. Some of these simple events can disturb the detection of complex events effectively acting as noise.  We apply the hierarchical approach in two different contexts defining the two different parts of our thesis.   In the first part, we present a mobile system that builds a representation of the user's life. This system is based on the episodic memory model, which is responsible for the storage and recollection of past experiences. Following the hierarchical approach, the system processes raw signals to detect simple events such as places where the user stayed a certain amount of time to perform an activity, therefore building sequences of detected activities. These activities are in turn processed to detect complex events that we call routines and that represent recurrent patterns in the life of the user.  In the second part of this thesis, we focus on the detection of glycemic events for diabetes type-1 patients in a non-invasive manner. Diabetics are not able to properly regulate their glucose, leading to periods of high and low blood sugar. We leverage signals (Electrocardiogram (ECG), accelerometer, breathing rate) from a sport belt to infer such glycemic events. We propose a physiological model based on the variations of the ECG when the patient has low blood sugar, and an energy-based model that computes the current glucose level of the user based on her glucose intake, insulin intake and glucose consumption via physical activity.  For both contexts, we evaluate our systems in term of accuracy by assessing wether the detected routines are meaningful, and wether the glycemic events are correctly detected, and in term of mobile performance, which confirms the fitness of our approaches for mobile computation.
000227938 6531_ $$awearable sensor
000227938 6531_ $$amobile computing
000227938 6531_ $$acomplex event
000227938 6531_ $$aevent recognition
000227938 6531_ $$aincremental algorithm
000227938 6531_ $$aepisodic memory
000227938 6531_ $$aroutine detection
000227938 6531_ $$anon-invasive glucose monitoring
000227938 700__ $$0246553$$g218869$$aRanvier, Jean-Eudes Marie
000227938 720_2 $$aAberer, Karl$$edir.$$g134136$$0240941
000227938 8564_ $$uhttps://infoscience.epfl.ch/record/227938/files/EPFL_TH7653.pdf$$zn/a$$s4567781$$yn/a
000227938 909C0 $$xU10405$$0252004$$pLSIR
000227938 909CO $$pthesis-bn2018$$pDOI$$pIC$$ooai:infoscience.tind.io:227938$$qDOI2$$qGLOBAL_SET$$pthesis
000227938 917Z8 $$x108898
000227938 917Z8 $$x108898
000227938 918__ $$dEDIC$$cIINFCOM$$aIC
000227938 919__ $$aLSIR
000227938 920__ $$b2017$$a2017-5-12
000227938 970__ $$a7653/THESES
000227938 973__ $$sPUBLISHED$$aEPFL
000227938 980__ $$aTHESIS