10,198 research outputs found
Robotic ubiquitous cognitive ecology for smart homes
Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work
DeePLT: Personalized Lighting Facilitates by Trajectory Prediction of Recognized Residents in the Smart Home
In recent years, the intelligence of various parts of the home has become one
of the essential features of any modern home. One of these parts is the
intelligence lighting system that personalizes the light for each person. This
paper proposes an intelligent system based on machine learning that
personalizes lighting in the instant future location of a recognized user,
inferred by trajectory prediction. Our proposed system consists of the
following modules: (I) human detection to detect and localize the person in
each given video frame, (II) face recognition to identify the detected person,
(III) human tracking to track the person in the sequence of video frames and
(IV) trajectory prediction to forecast the future location of the user in the
environment using Inverse Reinforcement Learning. The proposed method provides
a unique profile for each person, including specifications, face images, and
custom lighting settings. This profile is used in the lighting adjustment
process. Unlike other methods that consider constant lighting for every person,
our system can apply each 'person's desired lighting in terms of color and
light intensity without direct user intervention. Therefore, the lighting is
adjusted with higher speed and better efficiency. In addition, the predicted
trajectory path makes the proposed system apply the desired lighting, creating
more pleasant and comfortable conditions for the home residents. In the
experimental results, the system applied the desired lighting in an average
time of 1.4 seconds from the moment of entry, as well as a performance of
22.1mAp in human detection, 95.12% accuracy in face recognition, 93.3% MDP in
human tracking, and 10.80 MinADE20, 18.55 MinFDE20, 15.8 MinADE5 and 30.50
MinFDE5 in trajectory prediction
Ontology-based Activity Recognition Framework and Services
This paper introduces an ontology-based integrated framework for activity modeling, activity recognition and activity model evolution. Central to the framework is ontological activity modeling and semantic-based activity recognition, which is supported by an iterative process that incrementally improves the completeness and accuracy of activity models. In addition, the paper presents a service-oriented architecture for the realization of the proposed framework which can provide activity context-aware services in a scalable distributed manner. The paper further describes and discusses the implementation and testing experience of the framework and services in the context of smart home based assistive living
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