Applied Machine Learning for Assisted Living
portes grátis
Applied Machine Learning for Assisted Living
Uddin, Zia
Springer International Publishing AG
08/2022
131
Dura
Inglês
9783031115332
15 a 20 dias
389
Descrição não disponível.
1.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.
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machine learning;data;assisted living applications;human centered computing;text based event;user interfaces
1.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.
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