2.1. Smart Homes
2.2. Activity Recognition
- Offline classification algorithms, such as K-Nearest-Neighbors (K-NN) , Artificial Neural Networks (ANNs)  , Decision Trees (DTs)  or Support Vector Machines (SVMs) . These algorithms rely on the creation of “frames” of data of a chosen length that will try to find the closest example(s) in the database for a test frame. These algorithms are based only on statistical evaluation of the dispersion of the data in a given space. Using ontologies,  defines the context and the Activities of Daily Living for a further recognition with rules-based algorithms.
- Sequential algorithms, such as Hidden Markov Models (HMMs) , Conditional Random Fields (CRFs)  or also Markov Logic Networks (MLNs) . Those methods add to the previously cited algorithms a notion of dependence between the different events of the frame or of the activity. This allows to identify spatio-temporal relationships between the data that are totally absent in the classical methods. HMMs have been, for a long time, a reference method for activity recognition.
2.3. User Feedback for Smarter Homes
3. Adaptive and Personalized Smart Home Behavior
3.1. Global Architecture
- activity recognition,
- detecting implicit feedback, and exporting potential preferences through analyzing each received explicit feedback and each detected implicit feedback, and
- updating users profile using the detected new habits.
- an automated action by the smart home followed by a user explicit feedback is encountered, and
- when implicit feedback is encountered (a change of an actuator value by the user him/herself).
3.2. Description of the Installation of Sensors
- Sensors for all the possible electrical plugs of both rooms,
- Light, humidity and temperature sensors at different positions of the lab, and others for the outside,
- Sensors to measure the position of the shutter, the opening/closing of the doors and of the windows,
- Infra-red sensors, positioned at strategic locations in the rooms, to know where the person is (for activity recognition purpose, for instance),
- Actuators to command the different heaters of the rooms,
- Actuators to command the light (in four sets of lights distributed in the room),
- Actuators to turn on/off the different electrical outlets/plugs,
- Actuators to change the positions of the windows shutters,
- A sensor on the door that will, with an access card, recognize the person entering the room so that the system can load his/her profile (if already filled).
- Table and chairs to sit comfortably,
- A large smart TV to have access to different media such as music, TV, radio, YouTube channels, Internet, etc.,
- A baby foot table to play (several persons can be present in the living lab at the same time),
- A smart table using Microsoft Windows to allow people to use internet, read, etc. but also to give a feedback regarding smart home automation (see following sections).
3.3. Data Processing and Acquisition
- id is the identifier of the RDE in the database,
- type is the description of the type of the RDE,
- value describes the value of the RDE,
- timestamp is the timestamp describing the time that the changing of the RDE value took place.
- Check the state of the different sensors of the room and have information on the environment,
- Perform actions on the room such as turn on/off lights, power outlets, etc.
- Give an opinion on an action that just occurred in the room. For instance, if the smart home turns on the lights after sensing a decrease in the ambient luminosity and the user does not agree for some reason, he/she can notify it on the screen (thumbs up, thumbs down buttons).
3.4. Activity Recognition
3.5. Adaptive Decision Making
3.6. Learning from User Feedback
4. Preliminary Results
4.1. Parameters and Simulation of Situations
4.2. Simulation of Potential Preferences
- Re/Calculate the MDP policy.
- Generate n potential preferences.
- Evaluate the actions in the n potential preferences by counting the number of actions followed by a negative feedback called negative actions.
- Learn/Generalize and update the MDP reward function using the n new potential preferences.
- Repeat from step 1 until reaching number of traces.
4.4. Convergence Results
5. Future Orientations
5.1. Learning and Adaptation
5.2. Experimental Design
Conflicts of Interest
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|Model||SH Dataset||HIS Corpus|
|Without Unknown||With Unknown||diff.||Without Unknown||With Unknown||diff.|
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