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.