LEADER 05985nam 22006375 450 001 9910590058603321 005 20250507021137.0 010 $a9783031115349 010 $a3031115341 024 7 $a10.1007/978-3-031-11534-9 035 $a(CKB)5850000000062212 035 $a(MiAaPQ)EBC7078315 035 $a(Au-PeEL)EBL7078315 035 $a(OCoLC)1343121164 035 $a(PPN)264197631 035 $a(DE-He213)978-3-031-11534-9 035 $a(EXLCZ)995850000000062212 100 $a20220829d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplied Machine Learning for Assisted Living /$fby Zia Uddin 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (0 pages) 311 08$a9783031115332 311 08$a3031115333 320 $aIncludes bibliographical references. 327 $a1.Assisted Living -- 1. 1. Introduction -- 1.2. Surveys on Assisted Living -- 1.3. Assisted Living Projects -- 1.4. Target Users -- 1.4.1. Indoor Observations -- 1.4.2. Outdoor Observations -- 1.5. Privacy and Data Protection -- 1.6. Conclusion -- References -- 2. Sensors and Features for Assisted Living Technologies -- 2.1. Sensors in User care -- 2.1.1. Wearable Sensors -- 2.1.2. Smart Daily Objects -- 2.1.3. Environmental Sensors -- 2.1.2. Wearables with Ambient Sensors -- 2.1.3. Ambient Sensors in Robotic Assisted Living -- 2.2. Feature Extraction -- 2.2.1. Feature Extraction Using PCA -- 2.2.2. Kernel Principal Component Analysis (KPCA) -- 2.2.3. Feature Extraction Using ICA -- 2.2.4. Linear Discriminant Analysis (LDA) -- 2.2.5. Generalized Discriminant Analysis (GDA) -- 2.3. Discussion -- 2.4. Conclusion -- References -- 3. Machine Learning -- 3.1 Shallow Machine Learning -- 3.1.1. Support Vector Machines -- vii -- 3.1.2. Random Forests -- 3.1.3. AdaBoost and Gradient Boosting -- 3.1.4. Nearest Neighbors -- 3.1.5. Examples -- 3.2. Deep Machine Learning -- 3.2.1. Deep Belief Networks (DBN) -- 3.2.2. Convolutional Neural Network -- 3.2.3. Recurrent Neural Networks -- 3.2.4. Neural Structured Learning -- 3.2.4. Pre-trained deep learning models -- 3.3. Explainable AI (XAI) -- 3.3.1. Local Explanations -- 3.3.2. Rule-based Explanations -- 3.3.3. Visual Explanations -- 3.3.4. Feature Relevance Explanations -- 3.4. Discussion -- 3.5. Conclusion -- References -- 4. Applications -- 4.1. Wearable Sensor-based Behavior Recognition -- 4.1.1. MHEALTH Dataset -- 4.1.2. Experimental Results on MHEALTH Dataset -- 4.1.3. PUC-Rio Dataset -- 4.1.4. Experimental Results on PUC-Rio Dataset -- 4.1.5. ARem Dataset -- 4.1.6. Experimental Results on AReM Dataset -- 4.3. Video Camera-based Behavior Recognition -- 4.3.1. Binary Silhouettes and Features -- 4.3.2. Depth Silhouettes and Features -- 4.3.3. 3-D Model-based HAR -- 4.4. Other Ambient Sensor-based Behavior Recognition -- 4.4.1. CASAS Dataset -- viii -- 4.4.2. Experimental Results -- 4.5. Conclusion -- References. 330 $aUser care at home is a matter of great concern since unforeseen circumstances might occur that affect people's well-being. Technologies that assist people in independent living are essential for enhancing care in a cost-effective and reliable manner. Assisted care applications often demand real-time observation of the environment and the resident?s activities using an event-driven system. As an emerging area of research and development, it is necessary to explore the approaches of the user care system in the literature to identify current practices for future research directions. Therefore, this book is aimed at a comprehensive review of data sources (e.g., sensors) with machine learning for various smart user care systems. To encourage the readers in the field, insights of practical essence of different machine learning algorithms with sensor data (e.g., publicly available datasets) are also discussed. Some code segments are also included to motivate the researchers of the related fields to practically implement the features and machine learning techniques. It is an effort to obtain knowledge of different types of sensor-based user monitoring technologies in-home environments. With the aim of adopting these technologies, research works, and their outcomes are reported. Besides, up to date references are included for the user monitoring technologies with the aim of facilitating independent living. Research that is related to the use of user monitoring technologies in assisted living is very widespread, but it is still consists mostly of limited-scale studies. Hence, user monitoring technology is a very promising field, especially for long-term care. However, monitoring of the users for smart assisted technologies should be taken to the next level with more detailed studies that evaluate and demonstrate their potential to contribute to prolonging the independent living of people. The target of this book is to contribute towards that direction. 606 $aMedical informatics 606 $aMachine learning 606 $aUser interfaces (Computer systems) 606 $aHuman-computer interaction 606 $aHealth Informatics 606 $aMachine Learning 606 $aUser Interfaces and Human Computer Interaction 615 0$aMedical informatics. 615 0$aMachine learning. 615 0$aUser interfaces (Computer systems) 615 0$aHuman-computer interaction. 615 14$aHealth Informatics. 615 24$aMachine Learning. 615 24$aUser Interfaces and Human Computer Interaction. 676 $a362.40483 676 $a610.285631 700 $aUddin$b Ziya$01821353 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910590058603321 996 $aApplied Machine Learning for Assisted Living$94385217 997 $aUNINA