Body and Visual Sensor Fusion for GAIT Impairment Analysis in Ubiquitous Healthcare Systems (Record no. 8802)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 09195nam a22002537a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 210112b2011 a|||f mb|| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | EG-CaNU |
| Transcribing agency | EG-CaNU |
| 041 0# - Language Code | |
| Language code of text | eng |
| Language code of abstract | eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Mohamed Elsayed AbdelRaouf Elsayed |
| 245 1# - TITLE STATEMENT | |
| Title | Body and Visual Sensor Fusion for GAIT Impairment Analysis in Ubiquitous Healthcare Systems |
| Statement of responsibility, etc. | Mohamed Elsayed AbdelRaouf Elsayed |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Date of publication, distribution, etc. | 2011 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 122 p. |
| Other physical details | ill. |
| Dimensions | 21 cm. |
| 500 ## - GENERAL NOTE | |
| Materials specified | Supervisor: Mohamed A. El-Helw |
| 502 ## - Dissertation Note | |
| Dissertation type | Thesis (M.A.)—Nile University, Egypt, 2011 . |
| 504 ## - Bibliography | |
| Bibliography | "Includes bibliographical references" |
| 505 0# - Contents | |
| Formatted contents note | Contents:<br/>CHAPTER ONE ............................................................................................................... 19<br/>1. INTRODUCTION..................................................................................................... 19<br/>1.1. Introduction .................................................................................................. 19<br/>1.2. Challenges ..................................................................................................... 22<br/>1.3. Thesis Contribution ....................................................................................... 23<br/>CHAPTER TWO .............................................................................................................. 26<br/>2. PERVASIVE HEALTHCARE MONITORING ................................................................ 26<br/>2.1. Introduction .................................................................................................. 26<br/>2.2. Ubiquitous Monitoring Technologies ............................................................. 27<br/>2.2.1. Body Sensor Networks ........................................................................... 27<br/>2.2.2. Visual Sensor Networks ......................................................................... 30<br/>2.3. Sensor Design and Hardware Architectures ................................................... 33<br/>2.3.1. BSN Design Goals ................................................................................... 33<br/>2.3.2. BSNs Hardware Architecture .................................................................. 35<br/>2.3.3. VSNs Design Goals ................................................................................. 38<br/>2.3.4. VSNs Hardware Architecture.................................................................. 38<br/>2.4. Motion Analysis Applications ......................................................................... 41<br/>2.5. Conclusions ................................................................................................... 44<br/>CHAPTER THREE ........................................................................................................... 45<br/>3. BODY AND VISUAL MOTION FEATURES EXTRACTION AND FUSION ........................ 45<br/>3.1. Introduction ..................................................................................................<br/>3.2. Body Motion Feature Computation ............................................................... 45<br/>3.3. Visual Feature Extraction ............................................................................... 49<br/>3.3.1. Visual Data Enhancement ............................................................................ 49<br/>Background Modeling and Foreground Detection................................... 49<br/>Shadow Detection and Removal ............................................................. 53<br/>3.3.2. Blob Skeletonization .................................................................................... 56<br/>Moore Neighborhood algorithm ............................................................. 57<br/>3.3.3. Visual Metrics .............................................................................................. 60<br/>3.4. Data Fusion ................................................................................................... 64<br/>3.5. Conclusions ................................................................................................... 67<br/>CHAPTER FOUR ............................................................................................................. 68<br/>4. GAIT IMPAIRMENT CLASSIFICATION AND ANALYSIS .............................................. 68<br/>4.1. Introduction .................................................................................................. 68<br/>4.2. Classification Techniques ............................................................................... 68<br/>4.2.1. Multilayer Perceptron ................................................................................. 68<br/>4.2.2. Naïve Bayes Classifier .................................................................................. 71<br/>4.2.3. K-Nearest Neighbor ..................................................................................... 73<br/>4.2.4. Decision Tree............................................................................................... 75<br/>4.3. Change Analysis Technique............................................................................ 76<br/>4.4. Conclusion ..................................................................................................... 79<br/>CHAPTER FIVE ............................................................................................................... 80<br/>5. EXPERIMENTS AND RESULTS ................................................................................. 80<br/>5.1. Introduction .................................................................................................. 80<br/>5.2. Sensors Placement and Installation ............................................................... 81<br/>5.2.1. BSN Sensor Placement and Orientation ....................................................... 81<br/>5.2.2. VSN Nodes Deployment .............................................................................. 82<br/>5.3. Dataset Collection ......................................................................................... 85<br/>5.4. Cross-Validation Experiment.......................................................................... 87<br/>5.5. Gait Classification Experiment ....................................................................... 91<br/>5.4.1. Multiple Layer Preceptron ...........................................................................<br/>5.4.2. Naïve Bayes Classifier .................................................................................. 92<br/>5.4.3. K-Nearest Neighbors ................................................................................... 93<br/>5.4.4. Decision Tree............................................................................................... 95<br/>5.4.5. Error Percentage ......................................................................................... 96<br/>5.6. Gait Impairment Quantification using Change Analysis Technique ................. 97<br/>5.7. Conclusion ................................................................................................... 101<br/>CHAPTER SIX ............................................................................................................... 102<br/>6. CONCLUSIONS AND FUTURE WORK .................................................................... |
| 520 3# - Abstract | |
| Abstract | Abstract:<br/>Human motion analysis provides a valuable solution for monitoring the<br/>wellbeing of the elderly, quantifying post-operative patient recovery and monitoring<br/>the progression of neurodegenerative diseases such as Parkinson’s. The<br/>development of accurate motion analysis models, however, requires the integration<br/>of multi-sensing modalities and the utilization of appropriate data analysis<br/>techniques. This work describes a robust framework that is monitor and quantifies<br/>the patients’ gait impairment by integrating information captured by two different<br/>sensing modalities which are body and visual sensor networks. Real-time target<br/>extraction is applied and a skeletonization procedure is subsequently carried out to<br/>quantify the internal motion of moving target and computes two metrics,<br/>spatiotemporal cyclic motion between leg segments and head trajectory, for each<br/>vision node. Extracted motion metrics from multiple vision nodes and accelerometer<br/>information from a wearable body sensor are then fused at the feature level. Then<br/>the combined features used to classify target’s walking gait as being normal or<br/>abnormal (limping) by using Cross-Validation and several classification techniques<br/>such as Multiple Layer Preceptron, Naïve Bayes Classifier, K-Nearest Neighbors,<br/>and Decision Tree. Furthermore the proposed framework is capable of measuring<br/>gait impairment and recovery of postoperative patients in smart healthcare<br/>environments by using Change Analysis algorithm. The potential value of the<br/>proposed framework for patient monitoring is demonstrated and the results obtained<br/>from practical experiments are described. |
| 546 ## - Language Note | |
| Language Note | Text in English, abstracts in English. |
| 650 #4 - Subject | |
| Subject | Informatics-IFM |
| 655 #7 - Index Term-Genre/Form | |
| Source of term | NULIB |
| focus term | Dissertation, Academic |
| 690 ## - Subject | |
| School | Informatics-IFM |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Thesis |
| 650 #4 - Subject | |
| -- | 266 |
| 655 #7 - Index Term-Genre/Form | |
| -- | 187 |
| 690 ## - Subject | |
| -- | 266 |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Date acquired | Total Checkouts | Full call number | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Not For Loan | Main library | Main library | 01/12/2021 | 610/ ME.B 2011 | 01/12/2021 | 01/12/2021 | Thesis |