000			09195nam a22002537a 4500
008			210112b2011 a|||f mb|| 00| 0 eng d
040			_aEG-CaNU _cEG-CaNU
041	0		_aeng _beng
082			_a610
100	0		_aMohamed Elsayed AbdelRaouf Elsayed
245	1		_aBody and Visual Sensor Fusion for GAIT Impairment Analysis in Ubiquitous Healthcare Systems _cMohamed Elsayed AbdelRaouf Elsayed
260			_c2011
300			_a 122 p. _bill. _c21 cm.
500			_3Supervisor: Mohamed A. El-Helw
502			_aThesis (M.A.)—Nile University, Egypt, 2011 .
504			_a"Includes bibliographical references"
505	0		_aContents: CHAPTER ONE ............................................................................................................... 19 1. INTRODUCTION..................................................................................................... 19 1.1. Introduction .................................................................................................. 19 1.2. Challenges ..................................................................................................... 22 1.3. Thesis Contribution ....................................................................................... 23 CHAPTER TWO .............................................................................................................. 26 2. PERVASIVE HEALTHCARE MONITORING ................................................................ 26 2.1. Introduction .................................................................................................. 26 2.2. Ubiquitous Monitoring Technologies ............................................................. 27 2.2.1. Body Sensor Networks ........................................................................... 27 2.2.2. Visual Sensor Networks ......................................................................... 30 2.3. Sensor Design and Hardware Architectures ................................................... 33 2.3.1. BSN Design Goals ................................................................................... 33 2.3.2. BSNs Hardware Architecture .................................................................. 35 2.3.3. VSNs Design Goals ................................................................................. 38 2.3.4. VSNs Hardware Architecture.................................................................. 38 2.4. Motion Analysis Applications ......................................................................... 41 2.5. Conclusions ................................................................................................... 44 CHAPTER THREE ........................................................................................................... 45 3. BODY AND VISUAL MOTION FEATURES EXTRACTION AND FUSION ........................ 45 3.1. Introduction .................................................................................................. 3.2. Body Motion Feature Computation ............................................................... 45 3.3. Visual Feature Extraction ............................................................................... 49 3.3.1. Visual Data Enhancement ............................................................................ 49 Background Modeling and Foreground Detection................................... 49 Shadow Detection and Removal ............................................................. 53 3.3.2. Blob Skeletonization .................................................................................... 56 Moore Neighborhood algorithm ............................................................. 57 3.3.3. Visual Metrics .............................................................................................. 60 3.4. Data Fusion ................................................................................................... 64 3.5. Conclusions ................................................................................................... 67 CHAPTER FOUR ............................................................................................................. 68 4. GAIT IMPAIRMENT CLASSIFICATION AND ANALYSIS .............................................. 68 4.1. Introduction .................................................................................................. 68 4.2. Classification Techniques ............................................................................... 68 4.2.1. Multilayer Perceptron ................................................................................. 68 4.2.2. Naïve Bayes Classifier .................................................................................. 71 4.2.3. K-Nearest Neighbor ..................................................................................... 73 4.2.4. Decision Tree............................................................................................... 75 4.3. Change Analysis Technique............................................................................ 76 4.4. Conclusion ..................................................................................................... 79 CHAPTER FIVE ............................................................................................................... 80 5. EXPERIMENTS AND RESULTS ................................................................................. 80 5.1. Introduction .................................................................................................. 80 5.2. Sensors Placement and Installation ............................................................... 81 5.2.1. BSN Sensor Placement and Orientation ....................................................... 81 5.2.2. VSN Nodes Deployment .............................................................................. 82 5.3. Dataset Collection ......................................................................................... 85 5.4. Cross-Validation Experiment.......................................................................... 87 5.5. Gait Classification Experiment ....................................................................... 91 5.4.1. Multiple Layer Preceptron ........................................................................... 5.4.2. Naïve Bayes Classifier .................................................................................. 92 5.4.3. K-Nearest Neighbors ................................................................................... 93 5.4.4. Decision Tree............................................................................................... 95 5.4.5. Error Percentage ......................................................................................... 96 5.6. Gait Impairment Quantification using Change Analysis Technique ................. 97 5.7. Conclusion ................................................................................................... 101 CHAPTER SIX ............................................................................................................... 102 6. CONCLUSIONS AND FUTURE WORK ....................................................................
520	3		_aAbstract: Human motion analysis provides a valuable solution for monitoring the wellbeing of the elderly, quantifying post-operative patient recovery and monitoring the progression of neurodegenerative diseases such as Parkinson’s. The development of accurate motion analysis models, however, requires the integration of multi-sensing modalities and the utilization of appropriate data analysis techniques. This work describes a robust framework that is monitor and quantifies the patients’ gait impairment by integrating information captured by two different sensing modalities which are body and visual sensor networks. Real-time target extraction is applied and a skeletonization procedure is subsequently carried out to quantify the internal motion of moving target and computes two metrics, spatiotemporal cyclic motion between leg segments and head trajectory, for each vision node. Extracted motion metrics from multiple vision nodes and accelerometer information from a wearable body sensor are then fused at the feature level. Then the combined features used to classify target’s walking gait as being normal or abnormal (limping) by using Cross-Validation and several classification techniques such as Multiple Layer Preceptron, Naïve Bayes Classifier, K-Nearest Neighbors, and Decision Tree. Furthermore the proposed framework is capable of measuring gait impairment and recovery of postoperative patients in smart healthcare environments by using Change Analysis algorithm. The potential value of the proposed framework for patient monitoring is demonstrated and the results obtained from practical experiments are described.
546			_aText in English, abstracts in English.
650		4	_aInformatics-IFM _9266
655		7	_2NULIB _aDissertation, Academic _9187
690			_aInformatics-IFM _9266
942			_2ddc _cTH
999			_c8802 _d8802